Health care providers’ roles and responsibilities in management of polypharmacy: Results of a modified Delphi
Bibliographic record
Abstract
Background:Little is known about the roles that allow interprofessional teams to effectively manage older patients experiencing polypharmacy.Objectives:To identify and examine the consensus on salient interprofessional roles, responsibilities and competencies required in managing polypharmacy.Methods:Four focus groups with 35 team members practising in geriatrics were generated to inform survey development. The sessions generated 63 competencies, roles or responsibilities, which were categorized into 4 domains defined by the Canadian Interprofessional Health Collaborative. The resulting survey was administered nationally to geriatric health care professionals who were asked to rate the importance of each item in managing polypharmacy; we sought agreement within and across professions using a confirmatory 2-round Delphi method.Results:Round 1 was completed by 98 survey respondents and round 2 by 72. There was high intra-professional and interprofessional consensus regarding the importance of competencies among physicians, nurses and pharmacists; though pharmacists rated fewer competencies as important. Less consensus was observed among other health care professionals or they indicated the nonimportance of competencies despite focus group discussion to the contrary.Discussion:Although there is a strong consensus of polypharmacy management competencies across team members who have been more traditionally involved in medication management, there continue to be health care providers with differing understandings of competencies that may contribute to reduced reliance on medication. Lower importance ratings suggest pharmacists may not acknowledge or recognize their own potential roles in interprofessional polypharmacy management.Conclusion:Further exploration to understand the underutilization of professional expertise in managing polypharmacy will contribute to refining role clarity and translating competencies in practical settings, as well as guiding educators regarding curricular content.
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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 | 0.059 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".