Situations of anomie and the health workforce crisis: Policy implications of a socially sensitive and inclusive approach to human resources
Bibliographic record
Abstract
Health systems in most jurisdictions are facing an unprecedented workforce crisis, manifesting as labour shortages, high staff turnover, and increasing rates of absenteeism and burnout. These issues affect professional and occupational groups in both health and social care and individuals at early and later stages of their career. The intensity and pervasiveness of the crisis suggests that it is a multicausal phenomenon. Studies have focused on the relationship between working environments and worker satisfaction and well-being. However, these are of limited use in understanding the deeper mechanisms behind the large-scale workforce crisis. The subjective experience of work, while rooted in a particular work context, is also shaped by broader social and cultural phenomena that put social norms and individuals' ability to conform to them in tension. The concept of anomie, initially developed by Durkheim and redefined by Merton, focuses on the way social norms that guide conduct and aspirations lose influence and become incompatible with each other or unsuited to contemporary work contexts. Understanding the workforce crisis from the perspective of anomie enables the development and implementation of novel policies based on co-production strategies where concerned publics engage collaboratively in framing the problem and searching for solutions.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.043 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.022 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".