Orthopedist involvement in the management of clinical activities: a case study
Bibliographic record
Abstract
BACKGROUND: The rapid shift in hospital governance in the past few years suggests greater orthopedist involvement in management roles, would have wide-reaching benefits for the efficiency and effectiveness of healthcare delivery. This paper analyzes the dynamics of orthopedist involvement in the management of clinical activities for three orthopedic care pathways, by examining orthopedists' level of involvement, describing the implications of such involvement, and indicating the main responses of other healthcare workers to such orthopedist involvement. METHODS: We selected four contrasting cases according to their level of governance in a Canadian university hospital center. We documented the institutional dynamics of orthopedist involvement in the management of clinical activities using semi-structured interviews until data saturation was reached at the 37th interview. RESULTS: Our findings show four levels (Inactive, Reactive, Contributory and Active) of orthopedist involvement in clinical activities. With the underlying nature of orthopedic surgeries, there are: (i) some activities for which decisions cannot be programmed in advance, and (ii) others for which decisions can be programmed. The management of unforeseen events requires a higher level of orthopedist involvement than the management of events that can be programmed. CONCLUSIONS: Beyond simply identifying the underlying dynamics of orthopedists' involvement in clinical activities, this study analyzed how such involvement impacts management activities and the quality-of-care results for patients.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".