The ‘ <i>what’</i> and ‘ <i>how</i> ’ of screening for social needs in healthcare settings: a scoping review
Bibliographic record
Abstract
Background Adverse social determinants of health give rise to individual-level social needs that have the potential to negatively impact health. Screening patients to identify unmet social needs is becoming more widespread. A review of the content of currently available screening tools is warranted. The aim of this scoping review was to determine what social needs categories are included in published Social Needs Screening Tools that have been developed for use in primary care settings, and how these social needs are screened. Methods We pre-registered the study on the Open Science Framework ( https://osf.io/dqan2/ ). We searched MEDLINE and Embase from 01/01/2010 to 3/05/2022 to identify eligible studies reporting tools designed for use in primary healthcare settings. Two reviewers independently screened studies, a single reviewer extracted data. We summarised the characteristics of included studies descriptively and calculated the number of studies that collected data relevant to specific social needs categories. We identified sub-categories to classify the types of questions relevant to each of the main categories. Results We identified 420 unique citations, and 27 were included. Nine additional studies were retrieved by searching for tools that were used or referred to in excluded studies. Questions relating to food insecurity and the physical environment in which a person lives were the most frequently included items (92–94% of tools), followed by questions relating to economic stability and aspects of social and community context (81%). Seventy-five percent of the screening tools included items that evaluated five or more social needs categories (mean 6.5; standard deviation 1.75). One study reported that the tool had been ‘validated’; 16 reported ‘partial’ validation; 12 reported that the tool was ‘not validated’ and seven studies did not report validation processes or outcomes.
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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 | 0.070 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.038 | 0.034 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".