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The Process of Conducting Qualitative Grounded Theory Research for a Doctoral Thesis: Experiences and Reflections

2014· article· en· W231558186 on OpenAlexaff
Sophie Soklaridis

Bibliographic record

VenueThe Qualitative Report · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsGrounded theoryQualitative researchFocus groupConstructivist grounded theoryProcess (computing)SociologyPsychologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

In this article, the author offers her experiences as an example of the application of a grounded theory approach in qualitative research. She describes, in detail, her experiences of the process of collecting, analyzing, and storing data for her doctoral thesis. She focuses on the special challenges of employing a qualitative methodology to developing a conceptual framework. The substantive area in which the study took place was at a hospital-based occupational health clinic for professional artists. Various stakeholders involved in the clinic participated in in-depth individual interviews and focus groups to explore how the concept of integrative health care (IHC) is understood both in theory and in practice at the clinic.

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)0.220
metaresearch head score (Gemma)0.251
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.961

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2200.251
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0240.033
Scholarly communication0.0170.009
Open science0.0060.018
Research integrity0.0070.017
Insufficient payload (model declined to judge)0.0060.003

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.

Opus teacher head0.821
GPT teacher head0.754
Teacher spread0.067 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations35
Published2014
Admission routes1
Has abstractyes

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