Understanding the uptake of virtual care for first and return outpatient appointments in child and adolescent mental health services: a mixed-methods study
Bibliographic record
Abstract
OBJECTIVE: To describe patterns of virtual and in-person outpatient mental health service use and factors that may influence the choice of modality in a child and adolescent service. DESIGN: A pragmatic mixed-methods approach using routinely collected administrative data between 1 April 2020 and 31 March 2022 and semi-structured interviews with clients, caregivers, clinicians and staff. Interview data were coded according to the Consolidated Framework for Implementation Research (CFIR) and examined for patterns of similarity or divergence across data sources, respondents or other relevant characteristics. SETTING: Child and adolescent outpatient mental health service, Nova Scotia, Canada. PARTICIPANTS: IWK Health clinicians and staff who had participated in virtual mental healthcare following its implementation in March 2020 and clients (aged 12-18 years) and caregivers of clients (aged 3-18 years) who had received treatment from an IWK outpatient clinic between 1 April 2020 and 31 March 2022 (n=1300). Participants (n=48) in semi-structured interviews included nine clients aged 13-18 years (mean 15.7 years), 10 caregivers of clients aged 5-17 years (mean 12.7 years), eight Community Mental Health and Addictions booking and registration or administrative staff and 21 clinicians. RESULTS: During peak pandemic activity, upwards of 90% of visits (first or return) were conducted virtually. Between waves, return appointments were more likely to be virtual than first appointments. Interview participants (n=48) reported facilitators and barriers to virtual care within the CFIR domains of 'outer setting' (eg, external policies, client needs and resources), 'inner setting' (eg, communications within the service), 'individual characteristics' (eg, personal attributes, knowledge and beliefs about virtual care) and 'intervention characteristics' (eg, relative advantage of virtual or in-person care). CONCLUSIONS: Shared decision-making regarding treatment modality (virtual vs in-person) requires consideration of client, caregiver, clinician, appointment, health system and public health factors across episodes of care to ensure accessible, safe and high-quality mental healthcare.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".