Updating a survey for medication error prevention
Bibliographic record
Abstract
Objective. Literature review and subsequent gap analysis of the current Alberta Cancer Board (ACB) Oncology Medication Error Prevention Status Survey and the incorporation of new information to aid in the development of a stronger medication error prevention system. Design. Gap analysis based on a literature review was performed on the current ACB survey via a literature search of EMBASE, Medline, and the Cochrane Database of Systematic Reviews. The completed survey was sent to 17 ACB sites for feedback. Setting. The ACB in the Canadian province of Alberta, which includes 2 public tertiary centers and 15 associated community satellite sites based around the province in existing hospitals. Main outcome measures. Gaps in the current medication error prevention survey requiring improvement as compared to current literature, with emphasis on pharmacy. Results. All sections required additional information and two new sections were created to reduce the gaps in organizational commitment and environmental concerns. Of the 17 ACB sites, 13 sites responded to the survey and 11 responded to the questionnaire. Out of a possible 154 questions, 64 questions had at least one site disagree and 20 questions had more than one site disagree. Conclusion. Through a literature review and gap analysis, the current ACB Oncology Medication Error Prevention Status Survey was improved. Responses to changes have not only demonstrated the need for a survey of this kind, but also the need for periodic updates of the information in the survey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.089 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".