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Record W2026991048 · doi:10.1177/1049731509347864

How to Critically Evaluate Case Studies in Social Work

2009· article· en· W2026991048 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch on Social Work Practice · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsCredibilityDependabilityExternal validityQuality (philosophy)Data collectionPsychological interventionConstruct validityReliability (semiconductor)ValiditySocial workInternal validityApplied psychologyPsychologyConstruct (python library)Management scienceComputer scienceSocial psychologyPsychometricsMedicineClinical psychologySociologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.100
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.100
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.014
Science and technology studies0.0120.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.448
GPT teacher head0.626
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it