Systematic Bibliographic Database Searching for an Overview of Reviews: A Practical Guide Using Children’s Participation as a Case Study
Bibliographic record
Abstract
Abstract Systematic literature reviews are crucial in research. Identifying relevant research is the first stage in a systematic review, yet challenges exist hindering their efficacy. Through a case study search strategy addressing the question ‘What do we know about children’s participation in child welfare decision-making?’, this article seeks to promote efficiency in searching by building on existing conceptual and practical guidelines for conducting systematic literature searches and appraisal of database performance in social work research. Thirteen databases were utilised in this study. The total citations, unique hits, sensitivity and precision for each database were calculated to gauge database performance before conducting a cross-study comparison with five previously published social work systematic reviews to begin recognising emergent themes. Social Science Citation and PsycINFO are effective high-performing databases in social work. Social Services Abstracts, Applied Social Science Index and Abstracts are also recommended. The article emphasises the pitfalls of relying on a single database, highlighting the importance of comprehensive searches to avoid bias and increase relevance. The findings underscore the need for social work professionals to develop effective database searching skills, leveraging the information age to inform and enhance practice, promoting efficiency and addressing the challenges faced in this critical stage of research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.149 | 0.272 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.103 | 0.095 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.009 |
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".