Preparing social workers to address health inequities emerging during the COVID-19 pandemic by building capacity for health policy: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has brought tremendous changes in healthcare delivery and exacerbated a wide range of inequities. Social workers across a broad range of healthcare settings bring an expertise in social, behavioural and mental healthcare needed to help address these health inequities. In addition, social workers integrate policy-directed interventions and solutions in clinical practice, which is a needed perspective for recovery from the COVID-19 pandemic. It remains unclear, however, what the most pressing policy issues are that have emerged during the COVID-19 pandemic. In addition, many social workers in health settings tend to underuse policy in their direct practice. The objectives of this scoping review are to: (1) systematically scope the literature on social work, COVID-19 pandemic and policy; and (2) describe the competencies required by social workers and the social work profession to address the policy issues emerging during the COVID-19 pandemic. METHODS AND ANALYSIS: The scoping review follows Arksey and O'Malley's five-stage framework. Identification of literature published between 1 December 2019 and the search date, 31 March 2021, will take place in two stages: (1) title and abstract review, and (2) full-text review. In partnership with a health science librarian, the research team listed keywords related to social work and policy to search databases including Medline, Embase, PsycINFO, CINAHL, Social Services Abstract and Social Work Abstracts. Two graduate-level research assistants will conduct screening and full-text review. Data will then be extracted, charted, analysed and summarised to report on our results and implications on practice, policy and future research. ETHICS AND DISSEMINATION: Results will help develop a policy practice competence framework to inform how social workers can influence policy. We will share our findings through peer-reviewed publications and conference presentations. This study does not require Research Ethics Board approval as it uses publicly available sources of data.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".