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Record W2249785010 · doi:10.1177/1049732315605272

Balancing Methodological Rigor and the Needs of Research Participants

2015· article· en· W2249785010 on OpenAlexaff
Tak Mau Simon Chan, Eli Teram, Ian Shaw

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsWilfrid Laurier University
FundersHong Kong Baptist University
KeywordsRigourPerspective (graphical)PsychologyResearch designEngineering ethicsLimitingSociologyEpistemologySocial science

Abstract

fetched live from OpenAlex

Despite growing consideration of the needs of research participants in studies related to sensitive issues, discussions of alternative ways to design sensitive research are scarce. Structured as an exchange between two researchers who used different approaches in their studies with childhood sexual abuse survivors, in this article, we seek to advance understanding of methodological and ethical issues in designing sensitive research. The first perspective, which is termed protective, promotes the gradual progression of participants from a treatment phase into a research phase, with the ongoing presence of a researcher and a social worker in both phases. In the second perspective, which is termed minimalist, we argue for clear boundaries between research and treatment processes, limiting the responsibility of researchers to ensuring that professional support is available to participants who experience emotional difficulties. Following rebuttals, lessons are drawn for ethical balancing between methodological rigor and the needs of participants.

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)0.805
metaresearch head score (Gemma)0.830
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.195
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8050.830
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0120.007
Science and technology studies0.0120.047
Scholarly communication0.0120.016
Open science0.0090.022
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.0030.001

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 head0.979
GPT teacher head0.824
Teacher spread0.155 · 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
GenreMethods

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

Citations19
Published2015
Admission routes1
Has abstractyes

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