Preferences for virtual versus in-person mental and physical healthcare in Canada: a descriptive study from a cohort of youth and their parents enriched for severe mental illness
Bibliographic record
Abstract
BACKGROUND: Virtual care may improve access to healthcare and may be well suited to digitally connected youth, but experts caution that privacy and technology barriers could perpetuate access inequities. Success of virtual care will depend on its alignment with patient preferences. However, information on preferences for virtual and in-person healthcare is missing, especially for youth. We sought to quantify preferences for and barriers to virtual versus in-person mental and physical healthcare in youth and their parents, including in vulnerable segments of the population such as families with a parent with severe mental illness (SMI). METHODS: Participants were 219 youth and 326 parents from the Families Overcoming Risks and Building Opportunities for Wellbeing cohort from Canada, of which 61% of youth had at least one parent with SMI. Participants were interviewed about healthcare preferences and access to privacy/technology between October 2021 and December 2022. RESULTS: Overall, youth reported a preference for in-person mental (66.6%) and physical healthcare (74.7%) versus virtual care or no preference, and to a somewhat lesser degree, so did their parents (48.0% and 53.9%). Half of participants reported privacy/technology barriers to virtual care, with privacy being the most common barrier. Preferences and barriers varied as a function of parent SMI status, socioeconomic status and rural residence. CONCLUSIONS: The majority of youth and parents in this study prefer in-person healthcare, and the preference is stronger in youth and in vulnerable segments of the population. Lack of privacy may be a greater barrier to virtual care than access to technology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".