More than the sum of its parts? A qualitative research synthesis on multi-disciplinary primary care teams
Bibliographic record
Abstract
This qualitative research synthesis reviews interpretive scholarly papers on multi-disciplinary primary care teams. A bibliographic search was conducted in electronic databases: Medline, Embase, and the Web of Science Citation Index, and in the references of retrieved papers. The research consists of a taxonomic analysis of 19 qualitative studies about primary care teams published in peer-reviewed journals between 2001 and July 2008 in English and French. Nineteen qualitative studies were synthesized. Two major concerns emerged: (1) strategies for organizational change toward effective co-operative practice, and (2) dimensions of team interactions and work relations. The authors conclude that qualitative results suggest common strategies to improve the development of primary care teams, while identifying dimensions of team interactions that remain problematic. A fundamental aspect of team formation appears to be overlooked, i.e., the construction of a collective identity, which would involve the whole team in a shared ideal of co-operative practice. The adoption of discourse analysis is suggested as a more sophisticated qualitative methodology to explore this issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.075 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".