Formalisation and subordination: a contingency theory approach to optimising primary care teams
Bibliographic record
Abstract
OBJECTIVE: While there is consensus on the need to strengthen primary care capacities to improve healthcare systems' performance and sustainability, there is only limited evidence on the best way to organise primary care teams. In this article, we use a conceptual framework derived from contingency theory to analyse the structures and process optimisation of multiprofessional primary care teams. DESIGN: We focus specifically on inter-relationships between three dimensions: team size, formalisation of care processes and nurse autonomy. Interview-based qualitative data for each of these three dimensions were converted into ordinal scores. Data came from eight pilot sites in Quebec (Canada). RESULTS: We found a positive association between team size and formalisation (correlation score 0.55) and a negative covariation (correlation score -0.64) between care process formalisation and nurses' autonomy/subordination. Despite the study being exploratory in nature, such relationships validate the idea that these dimensions should be analysed conjointly and are coherent with our suggestion that using a framework derived from a contingency approach makes sense. CONCLUSIONS: The results provide insights about the structural design of nurse-intensive primary care teams. Non-physicians' professional autonomy is likely to be higher in smaller teams. Likewise, a primary care team that aims to increase nurses' and other non-physicians' professional autonomy should be careful about the extent to which it formalises its processes.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".