A mixed-methods exploration of the Real-Time Virtual Support pathway Child Health Advice in Real-Time Electronically in Northwestern BC
Bibliographic record
Abstract
Objectives: To explore the implementation of a provincial virtual paediatric consulting service, Child Health Advice in Real-Time Electronically (CHARLiE), integrated into the paediatric on-call schedule in Northwestern British Columbia. Methods: Healthcare providers in Northwestern British Columbia responded to a survey (n = 72) and participated in focus groups (n = 35) and key informant interviews (n = 4) to share their experiences engaging in a healthcare model that incorporated virtual paediatric consultants in lieu of in-person local paediatrician coverage over a 28-month period. Survey data was analyzed using descriptive statistics. Themes were generated from a qualitative descriptive approach to focus groups and key informant interview transcripts. Results: 96.1% of survey respondents who had used CHARLiE rated it as 'good' or 'very good' in the provision of overall support, while 77.6% reported that CHARLiE improved access to paediatric care. Focus group and key informant interview participants valued CHARLiE's timely, dedicated virtual bedside assessments of patients; collegiality and professionalism; amelioration of local paediatrician burnout; prevention of unnecessary transfers; and offloading of indirect patient care tasks. Video support improved provider confidence and appeared to improve caregiver trust. Suggested improvements included addressing technological barriers, enhancing providers' knowledge of local resources, and enabling continuity of care. Participants identified that virtual care does not replace on-the-ground specialist care. Conclusions: Participants valued CHARLiE's 24/7, timely, and collegial video support. While maintaining a full complement of on-the-ground paediatricians remains the goal for some rural communities, dedicated virtual support provides access to paediatric care in smaller communities, thereby improving health equity for children in British Columbia.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".