Perspectives on team communication challenges in caring for children with medical complexity
Bibliographic record
Abstract
BACKGROUND: Children with medical complexity (CMC) require the expertise of many care providers spanning different disciplines, institutions, and settings of care. This leads to duplicate health records, breakdowns in communication, and limited opportunities to provide comprehensive, collaborative care. The objectives of this study were to explore communication challenges and solutions/recommendations from multiple perspectives including (i) parents, (ii) HCPs - hospital and community providers, and (iii) teachers of CMC with a goal of informing patient care. METHODS: This qualitative study utilized an interpretive description methodology. In-depth semi-structured interviews were conducted with parents and care team members of CMC. The interview guides targeted questions surrounding communication, coordination, access to information and roles in the health system. Interviews were conducted until thematic saturation was reached. Interviews were audio-recorded, transcribed verbatim, and coded and analyzed using thematic analysis. RESULTS: Thirty-two individual interviews were conducted involving parents (n = 16) and care team members (n = 16). Interviews revealed 2 main themes and several associated subthemes (in parentheses): (1) Communication challenges in the care of CMC (organizational policy and technology systems barriers, inadequate access to health information, and lack of partnership in care) (2) Communication solutions (shared systems that can be accessed in real-time, universal access to health information, and partnered contribution to care). CONCLUSION: Parents, HCPs, and teachers face multiple barriers to communication and information accessibility in their efforts to care for CMC. Parents and care providers in this study suggested potential strategies to improve communication including facilitating communication in real-time, universal access to health information and meaningful partnerships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| 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".