The business of managing nurses’ substance‐use problems
Bibliographic record
Abstract
Nurses' experiences in, and the overall effectiveness of, widely used alternative-to-discipline programs to manage nurses' substance-use problems have not been adequately scrutinized. We uncovered the conflicted official and experiential ways of knowing one such alternative-to-discipline program in a Canadian province. We explicated this conflict through an institutional ethnography analysis. Ethnographic data from interviews with 12 nurses who were enrolled in an alternative-to-discipline treatment program and three program administrators, as well as institutional texts, were analyzed to explore how institutional practices and power relations co-ordinated and managed nurses' experiences. Analysis revealed the acritical acceptance of a standardized program not based on current norms of practice. Potential and actual conflicts of interest, power imbalances, and prevailing corporate interests were rife. Nurses were not afforded the same rights to quality ethical health care as other citizens. 'Expert' physicians' knowledge was privileged while nurses' knowledge was subordinated. Conclusions were that regulatory bodies cannot rely on the taken-for-granted standardized treatment model in widespread use. Individualized treatment alternatives reflecting current, scientific evidence must be offered to nurses, and nurses' knowledge, expertise, and experiences need to be included in decision-making processes in these programs.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".