Overview of factors influencing successful implementation of non‐medical prescribing
Bibliographic record
Abstract
Abstract Aim This study sought to determine the factors influencing successful implementation of non‐medical prescribing (NMP). Data sources EMBASE, Medline, CINAHL and reference lists were searched from January 2010 to November 2020. Study Selection Umbrella review and narrative synthesis of results were utilised. The retrieved reviews underwent title screening, abstract review, full‐text screening and assessment for inclusion. To guarantee the precision of the search results, Participants, Intervention, Control, and Outcomes (PICO) elements were recorded for each study. Studies were included if they were systematic reviews, published in English, published from January 2010 to November 2020, and discussed barriers and/or facilitators to NMP implementation. Results Of the 193 studies identified, eight were eligible for inclusion. Most of the reviews (62%) were published in 2017–2018. The majority of the reviews (62%) were focused on the United Kingdom. Three reviews discussed nurse prescribing, two reviews focused on pharmacist prescribing, and three reviews investigated NMP generally. Data were compiled into the Consolidated Framework for Implementation Research to evaluate the factors that influence the success or failure of NMP implementation. Conclusion Implementation of NMP is a complex process which requires fulfilment of all its elements. The success of NMP can be directly related to the extent of the whole system engagement and support, and available funding.
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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 | 0.029 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".