Transmission of vocational skills between experienced and new hospital workers
Bibliographic record
Abstract
OBJECTIVES: This article presents the results of a study currently underway looking at the transmission of vocational skills between health care workers in a French hospital. The aim was to show that health care workers, in addition to their work with patients, also have to incorporate the transmission of vocational skills into their daily activities. METHODS: Thirteen transmission situations were observed and analyzed by means of an activity-focused ergonomic work analysis, with the aim of reporting on this "invisible work". PARTICIPANTS: The population studied was composed of nurses and the nursing assistants from three different units in one hospital. RESULTS: The results show that the work required to integrate and supervise new staff members is left to the discretion of health care workers. This means they are constantly required to arbitrate on both an individual and collective basis between providing health care for patients and supporting new members of staff. The content of the transmission goes beyond the prescribed tasks and technical knowledge, as staff members also pass on their professional strategies (individual and collective), rules of practice and ethical considerations. Supervising students also offers experienced workers the opportunity to share their professional practices. CONCLUSIONS: This study highlights the issues arising from this transmission activity for the experienced workers, new workers, patients and the hospital.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".