Institutional Barriers to Healthy Workplace Environments: From the Voices of Social Workers Experiencing Compassion Fatigue
Bibliographic record
Abstract
Abstract The good health and well-being of health care professionals is increasingly an important issue and one that is under threat due to dominant neo-liberal economic factors. These factors influence health care service delivery which in turn focuses less on employee workplace satisfaction and more on profit-making corporate business models. More work with less pay/benefits, less time to work with clients and the focus on outcomes has created workplaces in which employees are experiencing negative organisational cultures that, in turn, affects their health and well-being. One negative effect is compassion fatigue (CF). In Canada, a national inter-disciplinary research project was conducted for health professionals (n = 52) who self-identified as experiencing CF. From this research, an analysis of a sub-sample of the data of fourteen social workers was conducted identifying specific institutional factors that participants described as creating conditions for their CF. These factors are presented including: (i) cost-effective services within time constraints and political climates; (ii) erosion of relationship building; (iii) lack of communication between managers and front line workers; (iv) cutbacks in services; (v) climate of fear; and (vi) outcome measurement requirements. These concerns related to workplace environments and the health and well-being of health professionals are discussed.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| 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".