Burnout and Professional Quality of Life Amongst Crisis Hotline Responders: A Cross-Sectional Survey in Canada During COVID-19
Bibliographic record
Abstract
Background/Objectives: There is a significant gap in accessibility to mental healthcare in Canada. This study aims to examine the population of Canadian crisis hotline responders and investigate the variables that contributed to burnout and professional quality of life during COVID-19. Crisis hotline responders are hypothesized to be affected by burnout and poor professional quality of life, due to the inherent nature of the job and the widespread negative mental health effects of COVID-19, which are expected to continue even after the pandemic. Methods: An online, cross-sectional, mixed-methods survey assessed sociodemographic information, shift-related variables, burnout and related factors, and current support methods utilized by crisis hotline responders across Canada. The open-ended questions helped to more personally reflect participants’ experiences. Data were analyzed using chi-square tests, an analysis of variance, and a regression analysis. Results: The survey was completed by 136 participants (78.7% female) with an average age of 39.68. Participants reported relatively high levels of burnout/stress on both the Copenhagen Burnout Inventory and professional quality of life survey. Younger age, less work experience, and working overnight shifts emerged as possible predictors of worse mental wellbeing. Conclusions: Findings suggest that Canadian crisis hotline responders require greater support to manage workplace burnout/stress. Nevertheless, conducting comprehensive studies during times when there are no public health emergencies are warranted to understand the full scope of burnout in this population. We offer five recommendations to support the mental wellbeing of responders and improve access to this important public health resource.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".