Technology-adaptable interventions for treating depression in adults with cognitive impairments: protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: Depression is a common comorbidity in individuals with cognitive impairment. Those with cognitive impairments face unique challenges in receiving the benefits of many conventional therapies for depression, and may have poorer outcomes in areas such as recovery and quality of life. However, the stigmatization of mental health disorders, cost barriers and physical disabilities may prevent these individuals from seeking mental health care. An online, self-help intervention specifically developed for adults with cognitive deficits and depression may be particularly beneficial to this population. We aim to inform the design of such an intervention through a systematic review by answering the following research question: among adults with cognitive impairment (including those with acquired brain injuries or neurodegenerative diseases), which technology-amenable interventions have been shown to effectively decrease symptoms of depression? Specifically, psychotherapeutic and/or behavioural interventions that could be delivered in a self-guided, online system will be included. METHODS: Comprehensive electronic searches will be conducted in MEDLINE, EMBASE, PsycINFO and CINAHL. Additional studies will be obtained through manually searching the references of relevant systematic reviews, contacting primary authors of select articles and tracking conference proceedings and trial registries. Article titles and abstracts will be screened using predefined eligibility criteria, and then judged for their amenability to the proposed self-help, technology-based intervention. The full text of those articles with selected interventions will then be screened to determine final eligibility for inclusion. Included articles will be categorized by intervention type and assessed for risk of bias using the Cochrane Effective Practice and Organization of Care Risk of Bias tool for non-randomized trials, controlled before-after studies and interrupted time series. The primary outcome will be a change in score on a validated depression scale, and adverse events will be documented as a secondary outcome. After data extraction from selected articles, pooling of data and meta-analysis will be conducted if a sufficient pool of studies with comparable methodology and quality are identified. Alternatively, plain language summaries will be developed. The quality of evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42014014417.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".