Tough Transitions: Family Caregiver Experiences with a Pediatric Long Term Ventilation Discharge Pathway
Bibliographic record
Abstract
Objectives: Discharging a child home on long term ventilation (LTV) via tracheostomy is complex and involves multiple healthcare providers across healthcare sectors. To date, patient and family feedback of a newly developed LTV discharge pathway has been anecdotal. Our objective was to explore the perceptions of family caregivers (FCs) that have completed the LTV pathway to home with respect to their: (1) experience with transitions across the pathway (2) perceptions of competency attainment and, (3) viewed opportunities for improvement. Methods: We conducted 11 semi structured interviews with FCs. Interviews focused on FCs experience with the training process, perception of competency from a knowledge and skill perspective and opportunities for improvement. Interviews were audiotaped, transcribed verbatim, coded and analyzed using an inductive thematic analysis approach. Results: Eight mothers and 3 fathers of 10 children participated. Six primary themes were identified: 1) making an informed decision, 2) transitioning to rehabilitation, 3) building capacity for self-care, 4) coordinating case management, 5) readying for discharge home and, 6) experiencing home care. Conclusion: Overall, FCs felt that the preparation and transition support obtained through the application of a standardized LTV discharge pathway allowed successful attainment of new knowledge and skills necessary to care for their child with LTV at home.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".