Consumer and Healthcare Professional Led Priority Setting for Quality Use of Medicines in People with Dementia: Gathering Unanswered Research Questions
Bibliographic record
Abstract
BACKGROUND: Historically, research questions have been posed by the pharmaceutical industry or researchers, with little involvement of consumers and healthcare professionals. OBJECTIVE: To determine what questions about medicine use are important to people living with dementia and their care team and whether they have been previously answered by research. METHODS: The James Lind Alliance Priority Setting Partnership process was followed. A national Australian qualitative survey on medicine use in people living with dementia was conducted with consumers (people living with dementia and their carers including family, and friends) and healthcare professionals. Survey findings were supplemented with key informant interviews and relevant published documents (identified by the research team). Conventional content analysis was used to generate summary questions. Finally, evidence checking was conducted to determine if the summary questions were 'unanswered'. RESULTS: A total of 545 questions were submitted by 228 survey participants (151 consumers and 77 healthcare professionals). Eight interviews were conducted with key informants and four relevant published documents were identified and reviewed. Overall, analysis resulted in 68 research questions, grouped into 13 themes. Themes with the greatest number of questions were related to co-morbidities, adverse drug reactions, treatment of dementia, and polypharmacy. Evidence checking resulted in 67 unanswered questions. CONCLUSION: A wide variety of unanswered research questions were identified. Addressing unanswered research questions identified by consumers and healthcare professionals through this process will ensure that areas of priority are targeted in future research to achieve optimal health outcomes through quality use of medicines.
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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 | 0.265 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".