Use of technology to provide mental health services to youth experiencing homelessness: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Despite the importance to address mental health issues as early as possible, youth experiencing homelessness (YEH) often lack prompt and easy access to health services. Recently, there has been a surge of studies focusing on leveraging technology to improve access to mental health services for YEH; however, limited efforts have been made to synthesise this literature, which can have important implications for the planning of mental health service delivery. Thus, this scoping review aims to map and synthesise research on the use of information and communication technologies (ICTs) to provide mental health services and interventions to YEH. METHODS AND ANALYSIS: A scoping review of the literature will be conducted, following Arksey and O'Malley's proposed methodology, the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews and recent guidelines from the Joanna Briggs Institute. All peer-reviewed papers using ICTs as a means of intervention will be considered, as well as grey literature. Only documents in English or French will be included in the analysis. First, 10 electronic databases will be consulted. Next, all data will be extracted into Covidence. Then, two reviewers will independently conduct the screening and data extraction process, in the case of discrepancies, a third reviewer will be included. Finally, data will be synthesised according to our objectives. ETHICS AND DISSEMINATION: Ethics approval is not required, as data will be collected from published literature. Findings will be disseminated through conference presentations and peer-reviewed journals.
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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 | 0.126 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.019 |
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".