Contradictions at work: struggles for control in Canadian health care
Bibliographic record
Abstract
The history of health care is in many ways a history of struggles for control of the work involved. These struggles have typically been highly gendered and racialised as well as class-based, with contradictory and complex consequences for both the care work and different workers. Indeed, some methods of managerial control are derived from reforms that have been fought for by workers, or have been built on their strategies. The nature and results of these struggles have changed over time and with place, shaped by global as well as local pressures. They have also changed with efforts to commodify health services, driven by profit-seeking and ideological motives, alongside management strategies introduced from the commercial sector into the organisation of healthcare work in the non-profit health services that remain. But there are real limits to the application of such strategies in health services, limits set not only by the organised resistance of healthcare workers but also by the nature of the work itself. As in Western Europe, health care in Canada has, at least until recently, largely escaped many of the forms of managerial control developed in the for-profit industrial sector. In this essay we focus on Canada, not because it is a special case but because it provides a concrete example of processes at work in many countries, and because context matters, with each country demonstrating some unique features.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.086 | 0.049 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".