Development of a medication management program for developmental support workers
Bibliographic record
Abstract
Background: Medication errors are a worldwide health care problem, which poses a risk to \npatients, caregivers, and the healthcare system. This risk can be mitigated through effective \nmedication management. At Momentum Developmental Support, developmental support \nworkers (DSWs) provide 24-hour residential care for adults with intellectual disabilities, \nprovince wide (Newfoundland). However, DSWs receive minimal training in medication \nmanagement. Consequently, this could lead to a rise in medication errors, negatively impacting \nclient safety. Purpose: The aim of this practicum project is to develop a medication management \nprogram to improve DSWs’ knowledge, confidence, and skills about medication management. \nMethods: Three key methods were used to develop the medication management resource: 1) an \nextensive literature review, 2) consultations with managers and DSWs at Momentum, and 3) an \nenvironmental scan of available resources. Results: The literature review identified a lack of \nmedication knowledge, confidence, and skills among DSWs, primarily attributed to the absence \nof hands-on training, which could potentially lead to medication errors. Consultations further \nconfirmed the lack of consistent training and resources for medication management, with a \npreference for classroom-based learning as the most effective teaching strategy. The \nenvironmental scan aligned with these findings, emphasizing the significance of combining \ntheoretical and practical learning in a classroom setting while highlighting the challenges \nassociated with online learning. Conclusion: A half-day medication management workshop was \ndeveloped to strengthen DSW’s knowledge, confidence, and skills about medication management.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".