Exploring What Motivates Parents of Children Living With Medical Complexity to Participate in Research
Bibliographic record
Abstract
BACKGROUND: The study aimed to understand the experience of and identify the motivations for parents participating in health research for their children with medical complexity (CMC). Patient-oriented research strategies are increasingly important in health research to ensure that the voices of patients and parents help shape and direct research programmes. To bring a family-centred and patient-oriented focus to our research and objectives, we asked parents about their experiences when they participated in healthcare research related to their child with CMC. METHODS: A parent partner, who also has a CMC, interviewed 12 parents (11 mothers and 1 father) of children living with medical complexity to understand their motivations to participate in healthcare research for their child. The parent partner conducted and transcribed the interviews and led our data analysis. Interpretive phenomenological analysis (IPA) was used to inform our data coding and analytic process. RESULTS: Parents described numerous reasons for their participation in research about their children. These motivations landed within four main themes: feeling helpless and hopeful, child-centred motivation, being part of something good and forming a relationship with the research team. In addition to these themes, parents highlighted factors that influenced their ability or desire to participate, such as time, capacity and the level of invasiveness for their child. Ultimately, the reflections by parents emphasized their unique lives in caring for their CMC and the need to integrate their lived experiences with the research they engage in. CONCLUSION: This study offers important insights for healthcare teams who want to engage parents of CMC to participate in research. Understanding parents' motivation to participate in research can help researchers create richer engagement and more meaningful experiences for themselves and their participants, thereby bolstering research programmes.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".