Impact of the COVID-19 pandemic on the mental health of those who identify as women of low socioeconomic status and living with diabetes: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: With the COVID-19 pandemic driving people into social isolation, causing a financial crisis and creating uncertainty, individuals were at an even greater risk of experiencing negative mental health outcomes. Individuals who identify as women living with diabetes mellitus (DM) of low socioeconomic status (SES) are potentially at increased risk of negative mental health outcomes secondary to health-related risks of COVID-19, as well as financial barriers to access to medications and diabetes-care supplies. OBJECTIVE: The objective of this scoping review is to investigate how the COVID-19 pandemic affected the mental health of those who identify as women living with DM of low SES including the consequences of public health measures put in place to stop the spread of the virus. The review aims to identify what is known about the impact of COVID-19 on this and identify potential areas for further investigation. METHODS AND ANALYSIS: published studies employing experimental and correlational designs to collect quantitative and/or qualitative data will be considered. Search strategies were developed for the MEDLINE, Embase and PsycINFO databases to identify relevant sources. Article titles and abstracts will be screened for eligibility by two independent reviewers. Full-text review will be conducted by two reviewers with a third reviewer being included if disagreement must be resolved. Data extraction will be conducted by two reviewers, one extraction and one quality check, and a third will resolve conflict if necessary. Data will be synthesised and reported in a narrative structure that provides a thematic analysis of the currently available literature. ETHICS AND DISSEMINATION: As this is a scoping review, there are no ethical approval requirements. There is to be a full publication of findings and analysis in a peer-reviewed journal.
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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 | 0.090 | 0.096 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 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".