Hindering and facilitating factors in the implementation of digital mental health interventions within community settings
Bibliographic record
Abstract
Introduction The digitalisation of the society has made inevitable the development and use of digital health. In mental health care, the use of digital tools has been questioned, although their capacity to improve accessibility to evidence-based information and tackle stigma has been recognised. The paradox of these virtual tools is that they need to rely on local resources to get used and disseminated. Objectives To identify the factors from the context that could help or hinder the set-up of an effective intervention in digital mental health. Methods Between 2018 and 2020, a digital mental health intervention, based on the promotion of StopBlues, a digital tool targeting psychic distress and suicide in in the adult general population, was conducted in 32 willing French localities. In each of the latter, a focal person was designated among the officials to organise the promotion locally and liaise with the research team. Employing interviews and observations, we identified the factors from the context that were favouring or hindering the intervention. Results The qualitative approach unveiled the existing dynamics between local stakeholders and difficulties faced by the focal persons. It appeared that the pollical context particularly influenced the outcome of the intervention. In parallel, the endorsement by local hospitals and psychiatrists was equally crucial confirming the key role they play when they champion a cause at the forefront. Conclusions Real-world evaluations using both qualitative and quantitative methods of digital mental health interventions have to be implemented in order to understand how they can help people. If these interventions are in line with the 1986 Ottawa Charter in terms of patient empowerment, they still need to be supported by local stakeholders, both at the pollical and medical levels. Disclosure of Interest None Declared
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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 | 0.039 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".