Improving Medication Safety in Cancer Services for Ethnic Minority Consumers: Protocol for a Pilot Feasibility and Acceptability Study of a Co-Designed Consumer Engagement Intervention
Bibliographic record
Abstract
BACKGROUND: People from ethnic minorities are often exposed to unsafe care contributing to poorer health care outcomes. Medication safety is a high-risk area requiring intervention to improve care outcomes. Using an adapted, experience-based co-design process with cancer service staff and patients from ethnic minorities, a medication communication tool was created: Making it Meaningful (MiM). OBJECTIVE: We aim to test whether the MiM tool is feasible and acceptable for use with ethnic minority consumers in cancer services in Australia. METHODS: A single site, controlled before and after this pilot study, will be used. Patients from Mandarin- and Russian-speaking backgrounds are eligible for inclusion. In total, 40 patients from these cultural backgrounds will be recruited and stratified by language to the intervention and control groups, with 20 participants in the intervention and 20 in the control group. Further, 4 health practitioners will be recruited and trained to use the MiM. Clinicians providing care for patients in the intervention will use the MiM during their usual appointment while providing medication communication using standard care processes for the control group. Telephone surveys will be conducted with participants at 3 time points, T1 before the intervention, T2 1 week post intervention, and T3 1 month post intervention, to assess knowledge and self-efficacy in medication management, perceived usability, and acceptability of the MiM. Qualitative interviews with clinicians who have used the MiM will be conducted 1 month postintervention to explore their perceptions of MiM feasibility and acceptability. RESULTS: Ethical approval for this research has been provided by the South Eastern Sydney Area Health Human Research Ethics Committee (HRECXXX). Bilingual field-workers, 1 Mandarin-speaking and 1 Russian-speaking, are contacting eligible patients to enroll. It is anticipated that recruitment will be completed by October 2023, with data collection completed by December 2023. CONCLUSIONS: Using experience-based co-design, we identified communication about medication, particularly between appointments, as a key issue impacting the safety of care for patients from ethnic minorities accessing cancer services. Increasing consumer engagement in medication management was identified as a strategy to reduce medication safety problems in cancer care; the MiM strategy was developed to address this issue. It is anticipated that by using the MiM, patient knowledge about prescribed medications and confidence in medication management will increase. Evidence from the pilot study will be used to inform a full-scale trial of the MiM tool with a range of ethnic minority communities accessing cancer services. A full-scale trial will seek to determine whether the MiM intervention is effective in knowledge and confidence about medication management, but also whether this improves patient outcomes in cancer care. TRIAL REGISTRATION: Australian New Zealand Clinical Trials ACTRN12622001260718p; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=384658&isReview=true. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49902.
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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 | 0.044 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".