Caregiver and Care Team Perceptions of Online Collaborative Care Planning for CMC
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Care plans summarize a child with medical complexity's (CMC) medical history and ongoing care needs. Often, the health care team controls the care plan content, limiting caregivers' ability to edit the document in real time and potentially compromising accuracy and utility. With this study, we aimed to provide caregivers of CMC with online access and shared editing control of their child's care plan and to explore the experiences of caregivers and care team members (CTMs) after using an online collaborative care plan (OCCP). METHODS: Caregivers of CMC were recruited from a tertiary complex care program to use an online, patient-facing platform for 6 months, which included the ability to edit and share their child's care plan. Caregivers and CTMs participated in semi-structured interviews to explore their experiences in using the OCCP. Consistent with grounded theory methodology, a constant comparative analysis was used, which allowed for theoretical sampling and theory generation. RESULTS: A total of 15 caregivers and 20 CTMs completed interviews. Interviews revealed 3 major themes and 9 subthemes, including (1) the navigation of uncharted roles (trust, responsibility), (2) the requirements for success (electronic medical record integration, online access, collaborative care plan review), and (3) cohesive care (accessibility and convenience, being on the same page, autonomy). Themes informed the creation of a theoretical model for the implementation and utility of OCCPs. CONCLUSIONS: Online, collaborative care plans, when implemented safely and thoughtfully, promote shared understanding, improve caregiver autonomy, and increase the accessibility of health information. Together, these benefits facilitate cohesive care and authentic partnership between caregivers and CTMs in the care of CMC.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".