Identifying Social Care Research Literature: Case Studies From Guideline Development
Bibliographic record
Abstract
Abstract Objective – Systematic searching is central to guideline development, yet guidelines in social care present a challenge to systematic searching because they exist within a highly complex policy and service environment. The objective of this study was to highlight challenges and inform practice on identifying social care research literature, drawing on experiences from guideline development in social care. Methods – The researchers reflected on the approaches to searching for research evidence to inform three guidelines. They evaluated the utility of major topic-focused bibliographic database sources through a) determining the yield of citations from the search strategies for two guidelines and b) identifying which databases contain the citations for three guidelines. The researchers also considered the proportion of different study types and their presence in certain databases. Results – There were variations in the ability of the search terms to capture the studies from individual databases, even with low-precision searches. These were mitigated by searching a combination of databases and other resources that were specific to individual topics. A combination of eight databases was important for finding literature for the included topics. Multiple database searching also mitigates the currency of content, topic and study design focus, and consistency of indexing within individual databases. Conclusion – Systematic searching for research evidence in social care requires considerable thought and development so that the search is fit for the particular purpose of supporting guidelines. This study highlights key challenges and reveals trends when utilising some commonly used databases.
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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 | 0.179 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.034 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.009 | 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".