Place des protocoles de coopération au sein de l’activité des pharmacies d’officine et leurs perspectives d’évolution : application concrète au sein du territoire de la CPTS de l’Adour
Bibliographic record
Abstract
In France, cooperation protocols allow health professionals to work together in a coordinated way to improve access to care and the quality of health services. The objective of this thesis is to understand the role of cooperation protocols within pharmacies and what are their prospects for development in the coming years. For this, we will rely on a concrete example: that of the CPTS (Multiprofessional Territorial Health Community) of Adour, located in two territories, the Landes and Gers, between Nouvelle-Aquitaine and Occitanie. One of the ongoing projects of CPTS is the implementation of a local cooperation protocol to take care, by the community pharmacist, of patients over 15 years old who come for dental pain. The development of cooperation protocols in France gives pharmacists the possibility to prescribe certain drugs. In order to see how these protocols and prescriptions by pharmacists could be evoluated in the coming years, and focus on the role of the prescribing pharmacist in six different countries: Quebec, Switzerland, USA, United KingdomUnited States, New Zealand and Australia. A questionnaire was also sent to these countries to obtain the testimony of pharmacists. Therefore, a reflection will be conducted on the prospects of evolution of cooperation protocols in France, also on the missions of pharmacists as prescribers.
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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 | 0.076 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".