Online tools for individuals with depression and neurologic conditions
Bibliographic record
Abstract
BACKGROUND: Patients with neurologic conditions commonly have depression. Online tools have the potential to improve outcomes in these patients in an efficient and accessible manner. We aimed to identify evidence-informed online tools for patients with comorbid neurologic conditions and depression. METHODS: A scoping review of online tools (free, publicly available, and not requiring a facilitator) for patients with depression and epilepsy, Parkinson disease (PD), multiple sclerosis (MS), traumatic brain injury (TBI), or migraine was conducted. MEDLINE, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, and Cochrane CENTRAL Register of Controlled Trials were searched from database inception to January 2017 for all 5 neurologic conditions. Gray literature using Google and Google Scholar as well as app stores for both Android and Apple devices were searched. Self-management or self-efficacy online tools were not included unless they were specifically targeted at depression and one of the neurologic conditions and met the other eligibility criteria. RESULTS: Only 4 online tools were identified. Of these 4 tools, 2 were web-based self-management programs for patients with migraine or MS and depression. The other 2 were mobile apps for patients with PD or TBI and depression. No online tools were found for epilepsy. CONCLUSIONS: There are limited depression tools for people with neurologic conditions that are evidence-informed, publicly available, and free. Future research should focus on the development of high-quality, evidence-based online tools targeted at neurologic patients.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".