Workplace bullying: measurements and metrics to use in the NHS. Final Report for NHS Employers.

The aim of this report is to identify how workplace bullying can be tracked over time, to indicate what measures and metrics can be used to identify change, and to provide comparators for other sectors in the UK and internationally. Bullying can encompass a range of different behaviours. Deciding on a definition of workplace bullying can clarify what is regarded as bullying, but it may also narrow the focus and exclude relevant issues of concern. For example, bullying definitions typically state that negative behaviours should be experienced persistently over a period of time. The threshold for behaviours to be defined as ‘bullying’ could be set to include one or two negative acts per month over the previous six months; or more stringently to include only behaviours that occur at least weekly over the previous twelve months. Choosing an appropriate threshold for frequency and duration of behaviours raises several questions: should occasional negative behaviours be regarded as bullying? Would one or two serious episodes of negative behaviour be regarded as bullying? Some researchers use the criteria of weekly negative behaviours over six months to identify bullying, but others argue that occasional exposure to negative acts can act as a significant stressor at work (Zapf et al., 2011). We have identified a range of tools and metrics that can be used to track change over time. However, there are a number of important issues to consider when measuring bullying which may affect the interpretation of the results. In particular, bullying prevalence rates vary considerably depending on the type of metric and definition of bullying used. For example, one international review found prevalence rates ranging from less than 1% for weekly bullying in the last six months up to 87% for occasional bullying over a whole career (Zapf et al., 2011). There are three main types of direct measures of bullying: self-labelling without a definition, self-labelling with a definition, and the behavioural experience method. Self-labelling metrics typically ask a respondent to identify themselves as a target of bullying (e.g., “Have you been bullied at work?” with a yes/no response, or “How often have you been bullied at work?” with a frequency scale such as never/occasionally/monthly/weekly/daily). This approach is quick and easy to administer, but is more subjective as responses will be based on the respondent’s interpretation of bullying. This approach can be improved with the provision of a definition of bullying, and a request to use the definition when responding. However, following pilot work, Fevre et al. (2011) argued that respondents tended not to read and digest bullying definitions as they had already decided what bullying meant to them. The behavioural experience method offers a more objective approach, but is typically longer and more time consuming. This method involves respondents rating the frequency with which they have experienced different negative behaviours (e.g., “How often has someone humiliated or belittled you in front of others?” with a frequency scale such as never/now and then/monthly/weekly/daily). These behavioural inventories may not mention bullying, but capture the prevalence of specific negative acts, and a total score may be calculated. The threshold for the frequency and number of negative acts, or a total score, required for an experience to be regarded as bullying can be chosen by the researcher. Although this enhances the objectivity of the measure, it may be that the respondent themselves may not regard their experience as bullying. In a meta-analysis of bullying studies conducted across 24 countries, Nielsen et al. (2010) found an overall prevalence rate of 18.1% for self-labelling with no definition, 11.3% for self-labelling with a definition, and 14.8% using a behavioural experience checklist. For best practice, it is recommended that both the self-labelling with a definition and the behavioural experience method are used in bullying research (Zapf et al., 2011). It is also important to be specific about the type of bullying being measured. In particular, if the measure is designed to capture bullying at work between co-workers this should be explicitly stated, so that bullying from patients and their relatives is excluded. Interpretation of the results may also be somewhat complex. Although increases in bullying prevalence should undoubtedly be addressed, we need to be mindful that an increase in reported bullying may reflect a change in culture: changing expectations of the behaviour of colleagues and managers, or a move towards greater openness and willingness to address concerns that were previously ignored or condoned. A measure of employees’ trust in the organisation to respond appropriately to such allegations may act as a positive indicator. The perceived and actual anonymity of responses is a critical factor. Employees are understandably wary about providing sensitive information on bullying and have voiced concerns regarding being identified and the potential repercussions of reporting bullying (Carter et al., 2013). There is a considerable discrepancy between the prevalence of bullying as captured in anonymous questionnaires and direct reports of bullying made to the organisation (e.g., to managers or HR; Scott, Blanshard & Child, 2008). Protecting the anonymity of respondents, and ensuring that individuals cannot be identified, will be important factors in the administration of a bullying measure. Some metrics are already routinely collected by the NHS, and if examined closely could provide useful indicators of change. Direct indicators include complaints about bullying and responses to ongoing NHS staff surveys. Indirect metrics can be used to capture factors that are associated with bullying, such as psychological wellbeing (including stress, anxiety and depression), sickness rates, job satisfaction and organisational commitment. However, factors other than bullying will affect these measures. The prevalence of witnessed bullying could also be considered as an important metric. A large proportion of NHS staff report that they have witnessed bullying between staff, and this is associated with negative outcomes for individuals and teams (Carter et al., 2013). Comparing the NHS prevalence rates with other sectors in the UK and internationally is complex. Ideally comparators would have used the same definition, measurement method and reporting period, but the definitions and metrics often differ. Total populations are the ideal, but are rarely provided. Single site studies are less generalisable than multi-site studies, and total samples are preferred over open invitations to unknown populations which may be more likely to attract responses from those who have experienced bullying. This report begins with several definitions of bullying, describes direct and indirect measures of bullying, and compares the prevalence of bullying in the NHS to other sectors in the UK, and to the healthcare sector internationally

Earthquake reconnaissance data sources, a literature review

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting data on building damage and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 39 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by these data. We conclude that modern reconnaissance missions cannot rely on a single data source. Different data sources should complement each other, validate collected data or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important data source

Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake

Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform

ニホンゴ ジュヨ ドウシ オヨビ ホウコウ ドウシ ノ カンセツ ワホウカ

久野(1978)は、日本語の間接話法文と視点現象との相関関係を考究し、間接話法節の中での視点制約違反を説明するため直接話法分析を提案している。次の文は氏の例である。（1）　a.　※太郎ハ僕ニオ金ヲ貸シテヤッタ。 　b.　太郎ハ、僕ニオ金ヲ貸シテヤッタト言イフラシテイル。 　c.　太郎ハ「僕ハ Ｘ ニオ金ヲ貸シテヤッタ」ト言イフラシテイル。(Ｘ＝文全体の話し手)（2）　a.　※僕ノ処ニ相談ニ行ケ。 　b.　太郎ハ花子ニ僕ノ処ニ相談ニ行ケト言ッテイルラシイ。 　c.　太郎ハ花子ニ「Ｘノ処ニ相談ニ行ケ」ト言ッテイルラシイ。(Ｘ＝文全体の話し手)久野は、(1a)と(2a)が不適格文であるのに、どうして(1b)と(2b)が適格文であるかを考察し、その的確性を(1c)と(2c)に見られるような目的節の直接話法表現レベルでの適格性の問題として説明している。氏は、間接話法節中の視点制約として次の仮説を提案している。(i)文全体の話し手が、その間接話法節の聞き手である場合には、視点制約は直接話法表現レベルよりは、間接話法表現レベルで充たされなければならない。(ii)文全体の話し手が、その間接話法節の聞き手でない場合には、視点制約は間接話法表現レベルよりは直接話法表現レベルで充たされなければならない。 本研究の目的は、授与動詞および方向動詞を含む日本語の間接話法文の適格性に関するネーティブスピーカーの判断の調査を実施し、上記久野の仮説を実証することにある。Kuno (1978: 273ff.) examines the empathy phenomena in indirect discourse in Japanese and proposes what he calls direct discourse analysis to account for conflict in the speaker\u27s empathy in indirectified benefactive and directional constructions. He uses the following examples:1. a. *Taroo-wa boku-ni okane-o kasi-te yat-ta. Taro has lent me money. b. Taroo-wa [boku-ni okane-o kasi-te yat-ta] to iihurasi-te i-ru. Taro is spreading the word that he has lent me money. c. Taroo-wa "Boku-wa X-ni okane-o kasi-te yat-ta" to iihurasi-te i-ru.[where X=the speaker of the entire speech] Taro appears to be saying to Hanako, "Go to X for advice." 2. a. *Boku-no tokoro-ni soodan-ni ik-e. Come to me for advice. b. Taroo-wa Hanako-ni [boku-no tokoro-ni soodan-ni ik-e] to it-te i-ru rasi-i. Taro appears to be telling Hanako that she should come to me for advice. c. Taroo-wa Hanako-ni "X-no tokoro-ni soodan-ni ik-e" to it-te i-ru rasi-i.[where X=the speaker of the entire speech] Taro appears to be saying to Hanako, "Go to X for advice." Kuno considers why 1b and 2b are acceptable while la and 2a are unacceptable and attempts to explain the acceptability of the former direct discourse; specifically, the Speech-Act Participant Empathy Hierarchy, in which the speaker has to empathize more with himself than with anyone else, is satisfied while 1b and 2b are still in direct discourse as are 1c and 2c, respectively. Kuno\u27s (1978: 276ff.) hypothesis depends upon who is the addressee of indirect discourse in the discourse level of speech where the Speech-Act Participant Empathy Hierarchy that should be satisfied is different. If the speaker of the entire speech is the addressee of indirect discourse, it should be satisfied more at the indirect discourse level than at the direct discourse level. If the speaker of the entire speech is not the addressee of indirect discourse, it should be satisfied more at the direct discourse level than at the indirect discourse level. This article is the result of a small-scale survey of native speakers\u27 acceptability judgments on indirect discourse sentences containing benefactive and directional verbs. The goal of the survey was to find out if there are any principles like Kuno\u27s hypothesis in which they prefer not to indirectify verbs in otherwise indirectified reported speech