A Framework for Expectations of Physician Commitment in Long-Term Care Homes: A Consensus Statement
Bibliographic record
Abstract
OBJECTIVES: Our objective was to establish consensus-guided expectations for primary care physician (PCP) commitment in Canadian long-term care (LTC) homes, developing a comprehensive framework for this multifaceted concept. DESIGN: This was a 2-round modified e-Delphi study with a virtual consensus meeting between rounds. SETTING AND PARTICIPANTS: Twenty-seven Canadian LTC PCPs as expert panelists (63% female; median age, 54 years; median 15 years of LTC experience) were included. METHODS: Panel members rated 38 candidate statements on relevance and feasibility using a 7-point scale, providing qualitative feedback in an online questionnaire. Consensus was defined a priori as ≥70% of panelists rating a statement in a particular direction. Nonconsensus statements in round 1 were revised and reevaluated in round 2 after the virtual meeting (study registration number: ISRCTN35125526). RESULTS: In round 1, 18 statements were endorsed as relevant and feasible expectations for PCP commitment. After the virtual meeting, 22 statements were rated in round 2, yielding 3 additional consensus statements. The final 21 endorsed statements encompassed the following: time allocated in LTC, in-person visits and on-site presence, number of residents cared for, assessments and care conferences, interdisciplinary collaboration, accessibility of PCP to staff, emphasis on medical care approaches for medication management and palliative care principles, and ongoing competency development. The panel did not endorse statements focused on the number of LTC homes a PCP serves, cumulative experience, specialized certifications, clinical leadership, or research activities as essential to commitment. CONCLUSIONS AND IMPLICATIONS: This first consensus-based framework for PCP commitment in Canadian LTC provides a clear, evidence-informed, multidimensional understanding, enhancing our ability to characterize and quantify physician involvement in LTC. This framework is crucial for enhancing care quality in the LTC sector, guiding policies and practices, influencing minimum care standards, and addressing health human resources while supporting future research linking PCP commitment to resident health outcomes and developing measurement tools to assess physician commitment in LTC.
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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 | 0.232 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.019 | 0.024 |
| Research integrity | 0.026 | 0.063 |
| Insufficient payload (model declined to judge) | 0.003 | 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".