Recruitment of caregivers into health services research: lessons from a user-centred design study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: With patient and public engagement in many aspects of the healthcare system becoming an imperative, the recruitment of patients and members of the public into service and research roles has emerged as a challenge. The existing literature carries few reports of the methods - successful and unsuccessful - that researchers engaged in user-centred design (UCD) projects are using to recruit participants as equal partners in co-design research. This paper uses the recruitment experiences of a specific UCD project to provide a road map for other investigators, and to make general recommendations for funding agencies interested in supporting co-design research. METHODS: We used a case study methodology and employed Nominal Group Technique (NGT) and Focus Group discussions to collect data. We recruited 25 family caregivers. RESULTS: Employing various strategies to recruit unpaid family caregivers in a UCD project aimed at co-designing an assistive technology for family caregivers, we found that recruitment through caregiver agencies is the most efficient (least costly) and effective mechanism. The nature of this recruitment work - the time and compromises it requires - has, we believe, implications for funding agencies who need to understand that working with caregivers agencies, requires a considerable amount of time for building relationships, aligning values, and establishing trust. CONCLUSIONS: In addition to providing adaptable strategies, the paper contributes to discussions surrounding how projects seeking effective, meaningful, and ethical patient and public engagement are planned and funded. We call for more evidence to explore effective mechanisms to recruit family caregivers into qualitative research. We also call for reports of successful strategies that other researchers have employed to recruit and retain family caregivers in their research.
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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 | 0.023 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it