Burnout Amongst Crisis Hotline Responders: A National Cross-Sectional Survey in Canada During COVID-19
Bibliographic record
Abstract
Introduction: There is a significant gap in accessibility to mental health care in Canada, worsened by various factors including rurality. Therefore, an important resource is crisis hotlines. Responders are hypothesized to be affected by the occupational phenomenon of burnout, partly due to the inherent nature of the job and partly due to the widespread negative mental health effects of the COVID-19 pandemic. Difficulties for crisis hotlines is expected to continue due to the ongoing fallout from the pandemic and from increased awareness of the Canada Suicide Prevention Line after introduction of a new 3-digit (9-8-8) number. This manuscript aims to characterize the population of Canadian crisis hotline responders and investigate the variables that contributed to burnout during COVID-19.; Methods: An online questionnaire assessed sociodemographic information, shift related variables, burnout, and current support methods utilized by crisis hotline responders across Canada. A qualitative component was also included to reflect participants’ experiences; Results: The cross-sectional assessment was completed by 136 participants. During COVID-19 Canadian crisis hotline responders reported relatively high levels of burnout/stress on both the Copenhagen Burnout Inventory and Professional Quality of Life Survey. Younger age emerged as the sole predictor of greater burnout amongst the variables we studied. The normal limitations of a cross-sectional survey apply. The COVID-19 pandemic may have affected the generalizability through several factors.; Conclusions: Findings suggest that Canadian crisis hotline responders, especially younger ones, require greater support to manage workplace burnout. 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.; Recommendations: Based on our data, we offer 5 recommendations to mitigate the risk of burnout for responders and improve access to this important public health resource.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".