Health professionals’ sentiments towards implemented information technologies in psychiatric hospitals: a text-mining analysis

Background Psychiatric hospitals are increasingly being digitalised. Digitalisation often requires changes at work for health professionals. A positive attitude from health professionals towards technology is crucial for a successful and sustainable digital transformation at work. Nevertheless, insufficient attention is being paid to the health professionals’ sentiments towards technology. Objective This study aims to identify the implemented technologies in psychiatric hospitals and to describe the health professionals’ sentiments towards these implemented technologies. Methods A text-mining analysis of semi-structured interviews with nurses, physicians and psychologists was conducted. The analysis comprised word frequencies and sentiment analyses. For the sentiment analyses, the SentimentWortschatz dataset was used. The sentiments ranged from -1 (strongly negative sentiment) to 1 (strongly positive sentiment). Results In total, 20 health professionals (nurses, physicians and psychologists) participated in the study. When asked about the technologies they used, the participating health professionals mainly referred to the computer, email, phone and electronic health record. Overall, 4% of the words in the transcripts were positive or negative sentiments. Of all words that express a sentiment, 73% were positive. The discussed technologies were associated with positive and negative sentiments. However, of all sentences that described technology at the workplace, 69.4% were negative. Conclusions The participating health professionals mentioned a limited number of technologies at work. The sentiments towards technologies were mostly negative. The way in which technologies are implemented and the lack of health professionals’ involvement seem to be reasons for the negative sentiments

The development of an automatic speech recognition model using interview data from long-term care for older adults

OBJECTIVE: In long-term care (LTC) for older adults, interviews are used to collect client perspectives that are often recorded and transcribed verbatim, which is a time-consuming, tedious task. Automatic speech recognition (ASR) could provide a solution; however, current ASR systems are not effective for certain demographic groups. This study aims to show how data from specific groups, such as older adults or people with accents, can be used to develop an effective ASR. MATERIALS AND METHODS: An initial ASR model was developed using the Mozilla Common Voice dataset. Audio and transcript data (34 h) from interviews with residents, family, and care professionals on quality of care were used. Interview data were continuously processed to reduce the word error rate (WER). RESULTS: Due to background noise and mispronunciations, an initial ASR model had a WER of 48.3% on interview data. After finetuning using interview data, the average WER was reduced to 24.3%. When tested on speech data from the interviews, a median WER of 22.1% was achieved, with residents displaying the highest WER (22.7%). The resulting ASR model was at least 6 times faster than manual transcription. DISCUSSION: The current method decreased the WER substantially, verifying its efficacy. Moreover, using local transcription of audio can be beneficial to the privacy of participants. CONCLUSIONS: The current study shows that interview data from LTC for older adults can be effectively used to improve an ASR model. While the model output does still contain some errors, researchers reported that it saved much time during transcription

Text mining in long-term care:Exploring the usefulness of artificial intelligence in a nursing home setting

OBJECTIVES: In nursing homes, narrative data are collected to evaluate quality of care as perceived by residents or their family members. This results in a large amount of textual data. However, as the volume of data increases, it becomes beyond the capability of humans to analyze it. This study aims to explore the usefulness of text mining approaches regarding narrative data gathered in a nursing home setting. DESIGN: Exploratory study showing a variety of text mining approaches. SETTING AND PARTICIPANTS: Data has been collected as part of the project 'Connecting Conversations': assessing experienced quality of care by conducting individual interviews with residents of nursing homes (n = 39), family members (n = 37) and care professionals (n = 49). METHODS: Several pre-processing steps were applied. A variety of text mining analyses were conducted: individual word frequencies, bigram frequencies, a correlation analysis and a sentiment analysis. A survey was conducted to establish a sentiment analysis model tailored to text collected in long-term care for older adults. RESULTS: Residents, family members and care professionals uttered respectively 285, 362 and 549 words per interview. Word frequency analysis showed that words that occurred most frequently in the interviews are often positive. Despite some differences in word usage, correlation analysis displayed that similar words are used by all three groups to describe quality of care. Most interviews displayed a neutral sentiment. Care professionals expressed a more diverse sentiment compared to residents and family members. A topic clustering analysis showed a total of 12 topics including 'relations' and 'care environment'. CONCLUSIONS AND IMPLICATIONS: This study demonstrates the usefulness of text mining to extend our knowledge regarding quality of care in a nursing home setting. With the rise of textual (narrative) data, text mining can lead to valuable new insights for long-term care for older adults

Encountering the Earth: political geological futures?

This chapter summarises some of the key ideas that have emerged in this volume, and seeks to do so by looking forward to future research. What kinds of research might follow on from the work in this book and elsewhere in the published literature? What themes are starting to emerge? The chapter initially reflects on the three sections that were selected for this volume and then suggests a number of other cross-cutting themes that span multiple disciplines and that will continue to develop as political geology moves forwards. These relate to geopolitics, the Anthropocene, histories and cartographies, technologies and the physical sciences more explicitly. Ultimately, the chapter aims to provoke debate and discussion about ways in which political geological studies can develop and influence policy, politics and philosophy