The Methodological Development of an Interprofessional Educational Program to Provide Proactive Integrated Care for Elders
Bibliographic record
Abstract
Background: Interprofessional collaboration in practice (IPCP) between professionals from the medical and social domain within primary care is desirable; however, it is also challenging due to fragmented healthcare. Little is known about the development of IPCP in primary care to fit the implementation context. This article describes the methodological development and the final content of an IPCP program.Methods and findings: The development process started with the identification of IPCP competencies in a literature review and a qualitative needs analysis with semi-structured interviews among eight elders and four health care professionals. The results were discussed during a first consultation with an expert team, which consisted of ten health care professionals. Consensus was reached on the themes role identity, communication, and shared vision development to form the basis of the program. A second consultation with the experts discussed the first version of the program. Then, consensus was reached on the final version of the program, which included a blended learning approach consisting of two face-to-face meetings, online learning, and on-the-job learning with a sixteen-hour time investment over a six-week period.Conclusions: The IPCP program was developed based on educational strategies and evidence, and with the support and knowledge of practice experts to fit the implementation context.
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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 | 0.065 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".