Farmaceuters syn på förskrivningsrätt för farmaceuter i Sverige - En pilotstudie
Bibliographic record
Abstract
Background: The topic of pharmacist prescribing in Sweden hasn’t been widely talked about or implemented like in the UK, New Zealand or Canada. The implementation of pharmacist prescribing has shown both benefits and drawbacks in different settings over the years. Aim: The aim of this study was to develop a questionnaire to explore pharmacists’ views on pharmacist prescribing in Sweden and their perception of potential facilitators and barriers with pharmacist prescribing. Methods: This study was divided into 3 parts to develop the questionnaire. The first part consisted of semi-structured interviews with pharmacists with diverse knowledge and work experience in the field. The questionnaire was tested by pharmacy students in the second part. In the last part of this study, the questionnaire was tested on pharmacists with diverse knowledge and work experience. The two latter steps focused on the validity of the questionnaire and were the pilot studies in this study. Results: The most frequently altered questions had to do with education and different situations the respondents would like to use pharmacist prescribing. The results from the interviews indicated that pharmacists have different opinions about pharmacist prescribing and the different type of pharmacist prescribing that should be implemented in Sweden. Conclusion: The participants found most of the questions in the questionnaire easy to interpret. However certain wordings were considered unclear to some extent. Thoughts about the type of prescribing rights and where pharmacist prescribing should be adopted are still not definite.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".