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Record W2100399818 · doi:10.1177/1049732304271832

Cooperation or Co-Optation?: Assessing the Methodological Benefits and Barriers Involved in Conducting Qualitative Research Through Medical Institutional Settings

2005· article· en· W2100399818 on OpenAlexaffabout
Deborah Parnis, Janice Du Mont, Brydon Gombay

Bibliographic record

VenueQualitative Health Research · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreTrent University
Fundersnot available
KeywordsPaternalismQualitative researchNegotiationInterviewSituatedNursingPsychologyPublic relationsMedicineSociologyMedical educationPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

In this article, the authors highlight some benefits of and barriers to doing qualitative research in association with hospital-based services. They first describe an ongoing qualitative research project that involves interviewing women about their post-sexual assault medicolegal experiences in hospital-situated sexual assault centers across a large Canadian province. Their methodological journey led them to engage program coordinators at these centers to assist with locating participants and qualified interviewers, and with negotiating the demands of their respective research ethics boards. They outline the ways in which their project was shaped, positively and negatively, by working with them in medical institutions. They conclude by recommending that hospitals and hospital ethics boards counteract tendencies toward paternalism by recognizing the value of feminist qualitative research contributions to the activities of their own sexual assault centers and to the recovery of sexually assaulted women. Such recognition might be productively engaged by adopting an ethics-in-process approach.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptMetaresearchScience and technology studies
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.554
metaresearch head score (Gemma)0.381
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.557
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.5540.381
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0090.016
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0010.000

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 head0.945
GPT teacher head0.797
Teacher spread0.148 · 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

Labeled directly by 2 models reading the full record.

MetaresearchScience and technology studies

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

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

Citations18
Published2005
Admission routes2
Has abstractyes

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