Looking and listening in a technical world : Effects of discontinuity in work schedules on nurses’ work activity
Bibliographic record
Abstract
The structure of work schedules and its effects on the work activity of health care workers in a short-term care facility were analyzed in a context of changes to the Québec health care system. The analysis revealed short patient stays and a great discontinuity in work periods for nursing personnel : 60 % of health care workers in one department worked for fewer than five shifts per month in this department. Thus, the workers often have little information on patients, and the constant changes in the work teams can affect the strategies that they use to remedy the situation. Since the “technical” aspects of the work cannot be reduced, the more “human” aspects can be compressed. Could this type of work organization diminish caring, thus depriving patients of important human contact and health care workers of their sense of the meaning of their work. Could this loss of meaning affect the absenteeism rate, which is known to be high among nurses ?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".