The effects of COVID-19 on European healthcare provision for people with major depressive disorder: a scoping review protocol
Bibliographic record
Abstract
Abstract Even before the pandemic, the treatment gaps in depression care were substantial, with issues ranging from rates of depression detection and intervention to a lack of follow-up after treatment initiation and access to secondary care services. The COVID-19 pandemic, which has had major effects on global healthcare systems, is almost certain to have impacted the MDD care pathway, though it is unclear what changes have manifested and what opportunities have arisen in response to COVID-19. The extent to which patients receive best-practice care is likely closely linked to clinical outcomes (and therefore disability burden) and as such, it is important to examine treatment gaps on the MDD care pathway during the pandemic. Here, we outline a protocol for a scoping review that investigates this broad topic, focusing on continuity of care and novel methods (e.g. digital approaches) used to mitigate care disruption. This scoping review protocol was designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) standards and will culminate in a narrative synthesis of evidence.
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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 | 0.105 | 0.106 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.074 | 0.013 |
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".