An exploratory study of nurses' knowledge of antipsychotic drug use with older persons
Bibliographic record
Abstract
Aim: Previous research has documented the widespread use of antipsychotic drugs by nursing staff with older persons, although less is known about the knowledge that nurses actually have about these drugs. The purpose of this exploratory, descriptive study was to survey a sample of UK gerontological nurses from different work settings on their knowledge of antipsychotic drugs.Methods: An exploratory descriptive study design was utilised, whereby a sample of nursing staff was given a questionnaire developed to determine knowledge about antipsychotic drugs and their use with older persons. Questionnaires were distributed to 100 nursing staff, including registered general nurses, registered mental nurses, state enrolled nurses, nursing assistants and care assistants. Of the 100 questionnaires distributed, 62 were returned and 57 were completed substantially enough for data analysis.Results: Descriptive statistics including frequencies and means were calculated for demographic variables and the questionnaire responses. Results indicated that the use of antipsychotic drugs within the psychiatric hospital setting was substantial, with 43.7% of patients receiving antipsychotic drugs, for an average length of time of 1.8 years. Conclusions: Nursing staff participants from all three work settings revealed a number of significant knowledge gaps, particularly with regard to appropriate indications for antipsychotic drugs with older persons and the side-effects of antipsychotic drugs. Summary: This paper adds new information regarding the use of antipsychotic drugs in the nursing care of older people.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".