A scoping review of decision-aid tools for disclosure and help-seeking of mental health concerns
Bibliographic record
Abstract
Globally, there is a large discrepancy between the prevalence of mental health concerns and the proportion of people who disclose or seek help for their mental health. As such, decision-aid tools have recently emerged in the mental health context to facilitate the disclosure and help-seeking process. Given recent developments in this field, a synthesis of the literature is needed to consolidate existing decision-aid tools and assess their effectiveness, particularly in facilitating the disclosure or help-seeking process. This scoping review aimed to capture and synthesise the growing literature on decision-aid tools designed to support people in the decision to disclose or seek help for their mental health concerns. The review considered empirical studies, including theses and dissertations that matched the following criteria: 1) focused on populations with a mental health concern, 2) reported the development and/or evaluation of a decision-aid tool, and 3) assessed a tool specifically designed to facilitate the disclosure or help-seeking process, or reported on at least one disclosure or help-seeking related outcome. The review was guided by Arksey and O'Malley's framework and the Joanna Briggs Institute's guidelines for scoping reviews. The findings of the review indicate that decision-aid tools can support various cognitive-emotional processes relevant to decision-making. There was also evidence demonstrating the effectiveness of decision-aid tools in increasing the rates of disclosure and help-seeking behaviours for mental health concerns. Overall, decision-aid tools appear to be a promising approach to enhance the effectiveness of disclosure and help-seeking decisions in the mental health context.
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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 | 0.040 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".