Youth Experiences With Referrals to Mental Health Services in Canada: Protocol for a Web-Based Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Youth mental health is an important public health concern affecting low-, middle-, and high-income countries, and many young people in need of mental health services do not receive the care they need when they need it. An early step in accessing mental health care is the referral process, yet most of the research done on pathways to care has focused on clinical populations (eg, first-episode psychosis) recruited from mental health care settings. There has been limited research attention on the experiences of referral to mental health services from the perspectives of youth recruited from the general population who may or may not have received the services they need. OBJECTIVE: This study aims to investigate the experiences that youth between the ages of 17 and 30 years have with referrals to mental health services and to better understand their perspectives on the use of technology to facilitate referrals. METHODS: This study will use a cross-sectional, Web-based survey design. A convenience sample of 400 participants from 3 Canadian provinces (Quebec, Ontario, and British Columbia), between the ages of 17 and 30 years, will be recruited via Facebook and will be invited to complete a Web-based survey anonymously. A questionnaire including a series of quantitative and qualitative questions will ask participants about their sociodemographic characteristics, past experiences with referral and access to mental health services, and opinions about using technology to facilitate the referral process. RESULTS: Participant recruitment is planned to be initiated by early January 2020 and is estimated to be completed by May 2020. Data will be analyzed using descriptive statistics and logistic regression or chi-square tests for quantitative data, and descriptive content analysis will be used for the qualitative data. CONCLUSIONS: The results of this study can help inform the improvement of referral policies and procedures in youth mental health service delivery. A better understanding of young people's perspectives on referral processes and their opinions on how these processes can be improved are essential to providing appropriate and timely access to mental health care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/16945.
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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 | 0.025 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".