CritCom: assessment of quality of interdisciplinary communication around deterioration in pediatric oncologic patients
Bibliographic record
Abstract
Background: High-quality clinical care requires excellent interdisciplinary communication, especially during emergencies, and no tools exist to evaluate communication in critical care. We describe the development of a pragmatic tool focusing on interdisciplinary communication during patient deterioration (CritCom). Methods: The preliminary CritCom tool was developed after a literature review and consultation with a multidisciplinary panel of global experts in communication, pediatric oncology, and critical care to review the domains and establish content validity iteratively. Face and linguistic validity were established through cognitive interviews, translation, and linguistic synthesis. We conducted a pilot study among an international group of clinicians to establish reliability and usability. Results: After reviewing 105 potential survey items, we identified 52 items across seven domains. These were refined through cognitive interviews with 36 clinicians from 15 countries. CritCom was piloted with 433 clinicians (58% nurses, 36% physicians, and 6% other) from 42 hospitals in 22 countries. Psychometric testing guided the refinement of the items for the final tool. CritCom comprised six domains with five items each (30 total). The final tool has excellent reliability (Cronbach's alpha 0.81-0.86), usability (93% agree or strongly agree that the tool is easy to use), and similar performance between English and Spanish tools. Confirmatory factor analysis was used to establish the final 6-domain structure. Conclusions: CritCom is a reliable and pragmatic bilingual tool to assess the quality of interdisciplinary communication around patient deterioration for children in diverse resource levels globally. Critcom results can be used to design and evaluate interventions to improve team communication.
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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".