Citizen and stakeholder led priority setting for long-term care research: identifying research priorities within the Translating Research in Elder Care (TREC) Program
Bibliographic record
Abstract
BACKGROUND: The Translating Research in Elder Care (TREC) program is a longitudinal partnered program of research in Western Canada that aims to improve the quality of care and quality of life for residents and quality of worklife for staff in long-term care settings. This program of research includes researchers, citizens (persons living with dementia and caregivers of persons living in long-term care), and stakeholders (representatives from provincial and regional health authorities, owner-operators of long-term care homes). The aim of this paper is to describe how we used priority setting methods with citizens and stakeholders to identify ten priorities for research using the TREC data. METHODS: We adapted the James Lind Alliance Priority Setting Partnership method to ensure our citizens and stakeholders could identify priorities within the existing TREC data. We administered an online survey to our citizen and stakeholder partners. An in-person priority setting workshop was held in March 2019 in Alberta, Canada to establish consensus on ten research priorities. The in-person workshop used a nominal group technique and involved two rounds of small group prioritization and one final full group ranking. RESULTS: We received 72 online survey respondents and 19 persons (citizens, stakeholders) attended the in-person priority setting workshop. The workshop resulted in an unranked list of their ten research priorities for the TREC program. These priorities encompassed a range of non-clinical topics, including: influence of staffing (ratios, type of care provider) on residents and staff work life, influence of the work environment on resident outcomes, and the impact of quality improvement activities on residents and staff. CONCLUSIONS: This modified priority setting approach provided citizens and stakeholders with an opportunity to identify their own research priorities within the TREC program, without the external pressures of researchers. These priorities will inform the secondary analyses of the TREC data and the development of new projects. This modified priority setting may be a useful approach for research teams trying to engage their non-academic partners and to identify areas for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".