Prioritising key motivators and challenges influencing informal carers’ decisions for participating in randomised trials: An embedded Study Within A before and after Trial (SWAT 55)
Bibliographic record
Abstract
Background: Family members, or others, often assume the role of informal (unpaid) carers of people with chronic illnesses. Care-giving, however, can impact profoundly on the quality of life of carers and can cause carer worry, stress and guilt. Implementing interventions that positively affect the lives of carers is important; however, carers as a group are often difficult to reach. We embedded a study within a pilot-feasibility trial of a mindfulness based intervention to determine and prioritise the key motivators and challenges influencing informal carers’ decisions for participating in a trial. Methods: We used a multi-method approach involving interviews with participants from a ‘ host trial’ and data from systematic reviews to develop a survey that was distributed to informal carers in Ireland. The survey consisted of 28 motivator and 17 challenge statements. Participants rated how important they thought each statement was when deciding to take part in a trial on a 5-point Likert Scale. Mean scores and standard deviations were calculated for each statement and arranged in descending order to provide the priority lists. Results: Thirty-six carers responded to the survey. Helping to create awareness about carers was the top ranked motivator, followed by four study design statements related to the time at which the study occurs, the study location, format of delivery and venue. The least important motivator related to how carers were invited to take part in a study. Difficulties in planning due to the caring role emerged as the most important challenge, followed by being unable to leave the care recipient on his/her own. Conclusions: Insight into decision-making for research participation will assist trial developers tailor trial processes for informal carer populations. We recommend that trialists should consider these motivators and challenges when designing future trials involving informal carers so as to enhance trial feasibility and success.
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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 | 0.121 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".