The role of sociodemographic factors on the acceptability of digital mental health care: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Many individuals experiencing mental health complications face barriers when attempting to access services. To bridge this care gap, digital mental health innovations (DMHI) have proven to be valuable additions to in-person care by enhancing access to care. An important aspect to consider when evaluating the utility of DMHI is perceived acceptability. However, it is unclear whether diverse sociodemographic groups differ in their degree of perceived acceptability of DMHI. OBJECTIVE: This scoping review aims to synthesize evidence on the role of sociodemographic factors (e.g., age, gender) in the perceived acceptability of DMHI among individuals seeking mental health care. METHODS: Guided by the JBI Manual of Evidence Synthesis, chapter on Scoping Review, a search strategy developed according to the PCC framework will be implemented in MEDLINE and then adapted to four electronic databases (i.e., CINAHL, MEDLINE, PsycINFO, and EMBASE). The study selection strategy will be piloted by two reviewers on subsets of 30 articles until agreement among reviewers reaches 90%, after which one reviewer will complete the remaining screening of titles and abstracts. The full-text screening, data extraction strategy, and charting tool will be completed by one reviewer and then validated by a second member of the team. Main findings will be presented using tables and figures. EXPECTED CONTRIBUTIONS: This scoping review will examine the extent to which sociodemographic factors have been considered in the digital mental health literature. Also, the proposed review may help determine whether certain populations have been associated with a lower level of acceptability within the context of digital mental health care. This investigation aims to favor equitable access to DMHI among diverse populations.
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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 | 0.155 | 0.129 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.054 | 0.014 |
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".