OHP-015 Clinical Research in France and Quebec
Bibliographic record
Abstract
<h3>Background</h3> Pharmacy practise is evolving in most countries. Hospital pharmacists are pivotal in the organisation and the support of clinical trials. We looked at the current state of pharmacy practise in clinical research. <h3>Purpose</h3> To identify differences in clinical research organisation and pharmacy practise between France and Quebec (Canada). <h3>Materials and Methods</h3> This is a descriptive study. A literature review was performed in order to describe the organisation of clinical research and the role of pharmacists in clinical research for both countries. Differences were identified by a panel consisting of one French pharmacy intern, one French hospital pharma cist, one Quebec research assistant and two Quebec hospital pharmacists. <h3>Results</h3> Fourteen differences relating to research organisation were identified. France and Canada have different normative frameworks, regulatory authorities, authorization processes, delays and shutdown processes. While it is encouraged, clinical trial registration is not mandatory in Canada. Data needs to be archived for 15 years in France vs. 25 years in Canada. Institutional review boards (IRB) have different names, location, composition, nomination processes, mandate duration and informed consent processes for minors. Seven key differences in pharmacy practise were identified. There are different authorization processes for drug compounding and manufacturing. Pharmacy fees are based on a national reference in France, but not in Canada. Software for the computerization of pharmacy services for clinical trials is common in France. In addition to drug trials, French pharmacists also manage sterile medical devices and medicinal products derived from human blood. Canadian pharmacists offer decentralised pharmaceutical care to hospitalised patients. Canadian pharmacists can be principal investigators if a doctor is the qualified investigator. <h3>Conclusions</h3> Clinical research organisation is similar on many aspects, but 21 main differences were identified. Comparisons between countries help identify best practise and may contribute to practise improvement. No conflict of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".