Caregiver knowledge and skills to safely care for pediatric tracheostomy ventilation at home
Bibliographic record
Abstract
OBJECTIVE: Caregivers of children using home mechanical ventilation (HMV) via tracheostomy require appropriate knowledge and skills. Existing training curricula are locally developed and content variable. We sought to develop a competency checklist to inform initial training and subsequent assessment of knowledge and skills of family caregivers. METHODS: We used a 2-step process. Candidate items were generated by synthesis of a scoping review, existing checklists, with additional items suggested by an eight member inter-professional group representing pediatric HMV programs across Canada. Following removal of duplicate items, we conducted a three-round Delphi to gain consensus on items for the KidsVent Checklist. RESULTS: The scoping review and checklists from five HMV programs identified 18 domains and 172 items; one additional domain and 83 additional items were identified by our expert group who also classified domains as mandatory or optional. We recruited 95 clinicians representing 12 Canadian paediatric HMV programs to participate in Delphi round 1 (response rate 72%; 84%, and 100% for subsequent rounds). Importance rating of the 255 items reduced them to 246 items. In the final checklist, the 19 domains comprised 14 mandatory (189 mandatory items) and 5 optional domains (57 optional items). CONCLUSIONS: We have developed the KidsVent checklist using rigorous consensus building methods, informed by participants with diverse geographic and inter-professional representation. This checklist represents knowledge and skills required to safely care for children using tracheostomy ventilation at home. Further study is required to explore the impact of this checklist on outcomes of this growing group of technology-dependent children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".