Patient engagement in an online coaching intervention for parents of children with suspected developmental delays
Bibliographic record
Abstract
Aim To evaluate patient engagement processes in the development of a new health coaching intervention for parents of children with suspected developmental delays. Method A cross‐sectional mixed‐method study design was used. Researchers (n=18) and patient‐partners (n=9) were surveyed using the Public and Patient Engagement Evaluation Tool (PPEET) in areas of: (1) communication/supports for participation; (2) sharing views/perspectives; (3) impacts/influence of engagement initiative; and (4) final thoughts/satisfaction. Descriptive statistics and an inductive thematic‐based approach were used to analyse the data. Results For both study groups, high agreement, with responses largely ranging between ‘agree’ to ‘strongly agree’, was noted on all four sections of the PPEET. Qualitative reports reflected that patient engagement was important, meaningful, and had a significant impact on the quality of the project and on the professional development of researchers in their understanding and use of patient‐oriented methodology. Patient‐partners noted challenges related to having realistic deadlines in providing feedback and a lack of a broader range of representation among members. Interpretation The benefits and challenges of applying patient‐oriented strategies to a multicentre trial were highlighted. These will be used to enhance our engagement processes. What this paper adds Researchers and patient‐partners reported successful and beneficial patient engagement. Enablers included members’ commitment, communication, partnership, and supports. Engagement barriers were mainly reported by patient‐partners rather than researchers. Barriers included communication issues, group homogeneity, and task management difficulties. Patient engagement enhanced the relevance and quality of the research project.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".