Observation of interprofessional collaboration in primary care practice: A multiple case study
Bibliographic record
Abstract
Interprofessional collaboration (IPC) is known to improve and enhance care for people with complex healthcare and social care needs and is ideally anchored in primary care. Such care is complex, challenging, and often poorly undertaken. In countries such as Canada, the United Kingdom, the Netherlands, Australia, and New Zealand, primary care is provided predominantly via general practices, where groups of general practitioners and nurses typically work. Using a case study design, direct observations were made of interprofessional activity in three diverse general practices in New Zealand to determine how collaboration is achieved and maintained. Non-participant observation of health professional interaction was undertaken and recorded using field notes and video recordings. Observational data were subject to analysis prior to collection of interview data, subsequently gathered independently at each site. Case-specific themes were developed before determining cross-case themes. Cross-case themes revealed five key elements to IPC: the built environment, practice demographics and location, practice business models, shared goals, and team structure and climate. The combination of elements at each practice site indicated that strengths in one area helped offset challenges in others. The three practices (cases) collectively demonstrated the importance of an "all of practice" commitment to collaborative practice so that shared decision-making can occur.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 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".