Effect of routinely assessing and addressing depression and diabetes distress using patient-reported outcome measures in improving outcomes among adults with type 2 diabetes: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Type 2 diabetes is a global health priority. People with diabetes are more likely to experience mental health problems relative to people without diabetes. Diabetes guidelines recommend assessment of depression and diabetes distress during diabetes care. This systematic review will examine the effect of routinely assessing and addressing depression and diabetes distress using patient-reported outcome measures in improving outcomes among adults with type 2 diabetes. METHODS AND ANALYSIS: MEDLINE, Embase, CINAHL Complete, PsycInfo, The Cochrane Library and Cochrane Central Register of Controlled Trials will be searched using a prespecified strategy using a prespecified Population, Intervention, Comparator, Outcomes, Setting and study design strategy. The date range of the search of all databases will be from inception to 3 August 2020. Randomised controlled trials, interrupted time-series studies, prospective and retrospective cohort studies, case-control studies and analytical cross-sectional studies published in peer-reviewed journals in the English language will be included. Two review authors will independently screen abstracts and full texts with disagreements resolved by a third reviewer, if required, using Covidence software. Two reviewers will undertake risk of bias assessment using checklists appropriate to study design. Data will be extracted using prespecified template. A narrative synthesis will be conducted, with a meta-analysis, if appropriate. ETHICS AND DISSEMINATION: Ethics approval is not required for this review of published studies. Presentation of results will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidance. Findings will be disseminated via peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42020200246.
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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 | 0.074 | 0.088 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.020 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.008 |
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".