Developing a national role description for medical directors in long-term care: survey-based approach.
Bibliographic record
Abstract
OBJECTIVE: To develop a national role description for medical directors in long-term care (LTC) based on role functions drawn from the literature and the LTC industry. DESIGN: A questionnaire about the role functions identified from the literature was mailed or e-mailed to randomly selected medical directors, directors of care or nursing (DOCs), and administrators in LTC facilities. SETTING: Long-term care facilities in Canada randomly selected from regional clusters. PARTICIPANTS: Medical directors, DOCs, and administrators in LTC facilities; a national advisory group of medical directors from the Long Term Care Medical Directors Association of Canada; and a volunteer group of medical directors. MAIN OUTCOME MEASURES: Respondents were asked to indicate, from the list of identified functions, 1) whether medical directors spent any time on each activity; 2) whether medical directors should spend time on each activity; and 3) if medical directors should spend time on an activity, whether the activity was "essential" or "desirable." RESULTS: An overall response rate of 37% was obtained. At least 80% of the respondents from all 3 groups (medical directors, DOCs, and administrators) highlighted 24 functions they deemed to be "essential" or "desirable," which were then included in the role description. In addition, the advisory group expanded the role description to include 5 additional responsibilities from the remaining 18 functions originally identified. A volunteer group of medical directors confirmed the resulting role description. CONCLUSION: The role description developed as a result of this study brings clarity to the medical director's role in Canadian LTC facilities; the functions outlined are considered important for medical directors to undertake. The role description could be a useful tool in negotiations pertaining to time commitment and expectations of a medical director and fair compensation for services rendered.
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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 | 0.055 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".