Licensed Social Workers’ Perception of the Role of the Supervisor and Its Impact on Stress in Social Work

This study explores how the role of the supervisor impacts social workers’ perceptions of stress in social work practice and how social workers measure the experience of supervision. The study sample consisted of 54 licensed social workers with different levels of licensure selected from the Minnesota Board of Social Work. A mixed method design, using both qualitative and quantitative methods, was used to collect data for this cross-sectional research study. An email with the link to the survey inQualtrics was sent to 160 licensed social workers. The data was assessed using descriptive statistics, chi-square analyses, and grounded theory methodology and coded based on constant comparison analysis. Findings from this study support previous research that identified that supervisors can both alleviate and create stress for supervisees. Findings also show that respondents consider the supportive role of the supervisor to be most beneficial to their practice, social workers perceive any social work job as stressful, and respondents are satisfied with the level of supportive supervision they receive from their supervisor. Furthermore, respondents perceive supportive supervision to be helpful and it generally has a positive impact on social workers’ work with clients. Supervisors will be able to understand and apply the findings to their practice to positively contribute to the supervisor-supervisee relationship. This will also positively impact the supervisee’s work with clients. In addition, social workers who are supervisors will be able to employ strategies based on the findings to decrease stress in social work practice as well as be more prepared to provide quality supervision and help staff members develop the skills needed for carrying out their work

Licensed Social Workers’ Perception of the Role of the Supervisor and Its Impact on Stress in Social Work

This study explores how the role of the supervisor impacts social workers’ perceptions of stress in social work practice and how social workers measure the experience of supervision. The study sample consisted of 54 licensed social workers with different levels of licensure selected from the Minnesota Board of Social Work. A mixed method design, using both qualitative and quantitative methods, was used to collect data for this cross-sectional research study. An email with the link to the survey inQualtrics was sent to 160 licensed social workers. The data was assessed using descriptive statistics, chi-square analyses, and grounded theory methodology and coded based on constant comparison analysis. Findings from this study support previous research that identified that supervisors can both alleviate and create stress for supervisees. Findings also show that respondents consider the supportive role of the supervisor to be most beneficial to their practice, social workers perceive any social work job as stressful, and respondents are satisfied with the level of supportive supervision they receive from their supervisor. Furthermore, respondents perceive supportive supervision to be helpful and it generally has a positive impact on social workers’ work with clients. Supervisors will be able to understand and apply the findings to their practice to positively contribute to the supervisor-supervisee relationship. This will also positively impact the supervisee’s work with clients. In addition, social workers who are supervisors will be able to employ strategies based on the findings to decrease stress in social work practice as well as be more prepared to provide quality supervision and help staff members develop the skills needed for carrying out their work

Copyright and open educational resources for CEGEP teachers

Comprend des références bibliographiques et webographique

Determining Optimal Routes That Balance Multiple Factors and Constraints

Current mechanisms to discover sightseeing routes in a given location or plan a multi-destination trip do not take into account all factors and constraints that are important to users. Users find it difficult to find routing that can optimally combine time, cost, and other constraints for multi-destination trips. This disclosure describes techniques for finding optimal routing that balances a number of relevant factors and constraints, such as distances, travel times, costs, traffic, travel modes, weather, scenery, crowd, noise, opening hours, etc. Users can seek optimal routing by providing a list of destinations or issuing a natural language query that can be processed via a large language model (LLM). For example, the LLM can generate a list of destinations based on a natural language query provided by the user. If users permit, the routing can be personalized by using a trained machine learning model that can consider a user’s individual preferences and context. The route can be augmented with personalized advertisements recommending relevant opportunities along the route

Chaining Kids to the Ever Turning Wheel: Other Contemporary Costs of Juvenile Court Involvement

In this essay, Candace Johnson and Mae Quinn respond to Tamar Birckhead’s important article The New Peonage, based, in part, on their work and experience representing youth in St. Louis, Missouri. They concur with Professor Birckhead’s conclusions about the unfortunate state of affairs in 21st century America— that we use fines, fees, and other prosecution practices to continue to unjustly punish poverty and oppressively regulate racial minorities. Such contemporary processes are far too reminiscent of historic convict leasing and Jim Crow era efforts intended to perpetuate second-class citizenship for persons of color. Johnson and Quinn add to Professor Birckhead’s critique by further focusing on the plight of children of color and surfacing non- financial sanctions in our juvenile courts that similarly marginalize minority youth. They argue these practices— including shackling, intentional and unintentional shaming, and educational deprivation—also work to reproduce a secondary caste in communities across the country

Automatic Creation of Map Layers Using Machine Learning

While map layers with different types of information are available, creating such layers currently requires substantial engineering effort and time. This disclosure describes the use of machine learning based clustering techniques for automatic generation of new layers in digital maps. Layers are automatically generated and can be inspected or corrected by human moderators. The layers include visualizations of aggregate geolocated data. The visualization can utilize generative AI for custom visuals. Layer creation is thus automated and faster, increasing the types of uses for digital maps. If the user permits, the automatically generated custom layers can include commerce-related layers or can be augmented by integrating the display of personalized advertisements and recommendations

Can variability in the effect of opioids on refractory breathlessness be explained by genetic factors?

© 2015, BMJ Publishing Group. All rights reserved. Objectives: Opioids modulate the perception of breathlessness with a considerable variation in response, with poor correlation between the required opioid dose and symptom severity. The objective of this hypothesis-generating, secondary analysis was to identify candidate single nucleotide polymorphisms (SNP) from those associated with opioid receptors, signalling or pain modulation to identify any related to intensity of breathlessness while on opioids. This can help to inform prospective studies and potentially lead to better tailoring of opioid therapy for refractory breathlessness. Setting: 17 hospice/palliative care services (tertiary services) in 11 European countries. Participants: 2294 people over 18 years of age on regular opioids for pain related to cancer or its treatment. Primary outcome measures: The relationship between morphine dose, breathlessness intensity (European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire; EORTCQLQC30 question 8) and 112 candidate SNPs from 25 genes (n=588). Secondary outcome measures: The same measures for people on oxycodone (n=402) or fentanyl (n=429). Results: SNPs not in Hardy-Weinberg equilibrium or with allele frequencies ( < 5%) were removed. Univariate associations between each SNP and breathlessness intensity were determined with Benjamini-Hochberg false discovery rate set at 20%. Multivariable ordinal logistic regression, clustering over country and adjusting for available confounders, was conducted with remaining SNPs. For univariate morphine associations, 1 variant on the 5-hydroxytryptamine type 3B (HTR3B) gene, and 4 on the β-2-arrestin gene (ARRB2) were associated with more intense breathlessness. 1 SNP remained significant in the multivariable model: people with rs7103572 SNP (HTR3B gene; present in 8.4% of the population) were three times more likely to have more intense breathlessness (OR 2.86; 95% CIs 1.46 to 5.62; p=0.002). No associations were seen with fentanyl nor with oxycodone. Conclusions: This large, exploratory study identified 1 biologically plausible SNP that warrants further study in the response of breathlessness to morphine therapy

The Emergence of Perceptual Category Representations During Early Development: A Connectionist Analysis

A number of recent studies on early categorization suggest that young infants form category representations for stimuli at both global and basic levels of exclusiveness (i.e., mammal, cat). A set of computational models designed to analyze the factors responsible for the emergence of these representations are presented. The models (1) simulated the formation of global-level and basic-level representations, (2) yielded a global-to-basic order of category emergence and (3) revealed the formation of two distinct global-level representations - an initial "self-organizing' perceptual global level and a subsequently "trained" arbitrary (i.e., non-perceptual) global level. Information from the models is used to make a number of testable predictions concerning category development in infants

Personalized Mixed Reality Audio Using Audio Classification Using Machine Learning

Traditional audio devices such as over-the-ear or in-ear headphones are limited in their ability to provide a personalized and engaging listening experience. When using such audio devices in a noisy environment, it can be difficult for a user to focus on the audio that is most important. This disclosure describes the use of mixed reality audio to enhance the listening experience via headphones, earbuds, or other audio devices during normal use. Machine learning techniques are used to modify the audio per user preferences, e.g., to focus the audio on a single person talking while the user is in a group, to turn down noisy/competing audio such as other people talking in a busy/noisy group setting like a crowd or party, to enhance muffled words with clean versions, etc. The modified audio is played back via headphones, earbuds, or other device to provide a personalized listening experience