How to Critically Evaluate Case Studies in Social Work
Bibliographic record
Abstract
The purpose of this article is to develop guidelines to assist practitioners and researchers in evaluating and developing rigorous case studies. The main concern in evaluating a case study is to accurately assess its quality and ultimately to offer clients social work interventions informed by the best available evidence. To assess the quality of a case study, we propose criteria, including transferability/external validity, credibility/internal validity, confirmability/construct validity, and dependability/reliability. Guidelines are presented in a phase-oriented framework: research design, data collection, and data analysis. Finally, several dimensions to enhance the quality at each phase of the guidelines in evaluating the case study are discussed.
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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 | 0.645 | 0.861 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.025 | 0.010 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.031 | 0.025 |
| Open science | 0.014 | 0.014 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".