Additional file 3 of A scoping review of perceptions from healthcare professionals on antipsychotic prescribing practices in acute care settings
Bibliographic record
Abstract
Additional file 3: Supplementary Table 1. Search strategy used in MEDLINE. Supplementary Table 2. Characteristics of included studies. Supplementary Table 3. Antipsychotic reported outcomes of included studies. Supplementary Table 4. Reportedantipsychotic medication prescribing indications included studies by acute care setting. Supplementary Table 5. Measured and perceived antipsychotics prescribed and prescribing indications reported for included studies, by acutecare setting. Supplementary Table 6. Number of studies reporting on healthcare professional reported perceived antipsychotic prescribing practices in acute care, by acute care setting and antipsychotic type. Supplementary Table 7. Number of studies reporting on measured outcomes of antipsychotic prescribing practices in the acute care setting, by acute care setting and antipsychotic type. Supplementary Table 8. Reported additionally prescribed sedative hypnotic medications for included studies reporting on antipsychotic medication prescribing, by acute care setting. Supplementary Table 9. Reported co-prescribed sedative hypnotic medications with antipsychotic medications for included studies which report on additionally prescribed medications, by acute care setting. Supplementary Table 10. Domains and constructs according to the Theoretical Domains Framework of perspectives on antipsychotic prescribing from healthcare professionals for included studies, by acute care setting. Supplementary Table 11. Deductive thematic analysis using the Theoretical Domains Framework on perceptions on antipsychotic prescribing forincluded studies. Supplementary Table 12. Description of reported antipsychotic deprescribing strategies applied in parallel for included studies reporting on antipsychotic medication prescribing.
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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 | 0.006 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.806 | 0.049 |
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".