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Record W2156074058 · doi:10.1177/104973200129118624

Researching Illness and Injury: Methodological Considerations

2000· article· en· W2156074058 on OpenAlexaff
Janice M. Morse

Bibliographic record

VenueQualitative Health Research · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsQualitative researchPsychologyReliability (semiconductor)Everyday lifePerceptionData collectionApplied psychologySocial psychologyEpistemologySociologySocial sciencePower (physics)

Abstract

fetched live from OpenAlex

Circumstances surrounding the physical condition of the critically ill, the injured, and the dying make the conduct of qualitative research particularly difficult. Assumptions embedded in qualitative research are challenged or no longer apply: As sick people, participants are unfamiliar with their everyday worlds, and they are often incapable of describing their conditions and perceptions, so that researchers have difficulty obtaining data to comprehend, interpret, and generally conduct their research. Methodological problems extending from the participants' condition include the lack of everyday language to describe their experiences, the instability of the participants' reality, and the instability of the self. When researching participants who are sick, these methodological problems result in decisions about the timing of data collection, challenges to validity and reliability, and debates about who should be conducting this 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)0.656
metaresearch head score (Gemma)0.727
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.344
Threshold uncertainty score0.425

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6560.727
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0190.017
Science and technology studies0.0130.028
Scholarly communication0.0170.017
Open science0.0120.012
Research integrity0.0110.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.932
GPT teacher head0.799
Teacher spread0.133 · 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 designTheoretical or conceptual
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

Citations72
Published2000
Admission routes1
Has abstractyes

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