Assessing the Evidence for e-Resources for Mental Health Self-Management: A Systematic Literature Review
Bibliographic record
Abstract
BACKGROUND: In a climate which recognizes mental health as a key health improvement target, but where mental health services are increasingly over-stretched, self-management e-resources can play a potentially important role in helping to ensure people get the care and support they need. They have the potential to enable individuals to learn more about, and to exercise active involvement in, their care, and thus we see a growing interest in this area for both research and practice. However, for e-resources to become important adjuncts to clinical care, it is necessary to understand if and how they impact on patients and care outcomes. OBJECTIVE: The objective of this study was to review systematically the research evidence for theory-driven and evidence-based mental health self-management e-resources; and make recommendations about strengthening the future evidence base. METHODS: A comprehensive literature search in MEDLINE, EMBASE, AMED, PsycINFO, Scopus, and Cochrane Library was conducted. No limits to study design were applied. We did not restrict the types of Web-based technologies included, such as websites and mobile applications, so long as they met the study inclusion criteria. A narrative synthesis of data was performed to elaborate both the development and effectiveness of online resources. RESULTS: In total, 2969 abstracts were identified. Of those, 8 papers met the inclusion criteria. Only one randomized controlled trial was identified. The e-resources were aimed at self-management of bipolar disorder, depression, or general mental health problems. Some of the e-resources were intended to be used as prevention aids, whereas others were recovery orientated. CONCLUSIONS: Mental health self-management e-resources have the potential to be widely effective, but our review shows it is early days in terms of development of the evidence base for them. To build robust evidence, clear guidelines are needed on the development and reporting of e-resources, so that both developers and researchers maximize the potential of a new, but rapidly evolving area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".