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Record W2767531743 · doi:10.1080/08841233.2017.1386259

An Empirical Appraisal of Canadian Doctoral Dissertations Using Grounded Theory: Implications for Social Work Research and Teaching

2017· article· en· W2767531743 on OpenAlexaffabout
Morgan Braganza, Bree Akesson, David W. Rothwell

Bibliographic record

VenueJournal of Teaching in Social Work · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Applications
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsGrounded theoryCredibilityQualitative researchEmpirical researchSocial workQuality (philosophy)ChecklistEducational researchSociologyPsychologyPedagogySocial sciencePolitical scienceEpistemology

Abstract

fetched live from OpenAlex

Grounded theory is a popular methodological approach in social work research, especially by doctoral students conducting qualitative research. The approach, however, is not always used consistently or as originally designed, compromising the quality of the research. The aim of the current study is to assess the quality of recent Canadian social work doctoral dissertations implementing a grounded theory approach. Our analysis is based on the premise that presentations of grounded theory approaches in doctoral dissertations impact the conduct of teaching and future research and have direct implications for the legitimacy of qualitative research. Using Saini and Shlonsky’s Qualitative Research Quality Checklist, the authors appraised dissertations in terms of credibility, dependability, confirmability, transferability, authenticity, and relevance. The article concludes with implications regarding the quality of studies utilizing grounded theory approaches and consequences for future doctoral education and 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 imitation

Not 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.

metaresearch head score (Codex)0.127
metaresearch head score (Gemma)0.316
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1270.316
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0300.039
Science and technology studies0.0270.013
Scholarly communication0.0180.004
Open science0.0030.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.545
GPT teacher head0.667
Teacher spread0.123 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes2
Has abstractyes

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