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Record W2141873728 · doi:10.1177/1539449215576488

Extending Beyond Qualitative Interviewing to Illuminate the Tacit Nature of Everyday Occupation

2015· article· en· W2141873728 on OpenAlexaffabout
Suzanne Huot, Debbie Laliberté Rudman

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsWestern University
Fundersnot available
KeywordsQualitative researchInterviewVariety (cybernetics)NarrativeVisual researchOccupational therapyEthnographyTacit knowledgeCitizen journalismOccupational scienceSociologyPsychologyEngineering ethicsEpistemologyKnowledge managementSocial scienceComputer scienceEngineeringVisual artsArtificial intelligence

Abstract

fetched live from OpenAlex

The study of human occupation requires a variety of methods to fully elucidate its complex, multifaceted nature. Although qualitative approaches have commonly been used within occupational therapy and occupational science, we contend that such qualitative research must extend beyond the sole use of interviews. Drawing on qualitative methodological literature, we discuss the limits of interview methods and outline other methods, particularly visual methods, as productive means to enhance qualitative research. We then provide an overview of our critical ethnographic study that used narrative, visual, and observational methods to explore the occupational transitions experienced by immigrants to Canada. We describe our use of occupational mapping and participatory occupation methods and the contributions of these combined methods. We conclude that adopting a variety of methods can enable a deeper understanding of the tacit nature of everyday occupation, and is key to advancing knowledge regarding occupation and to informing occupational therapy practice.

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 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)0.035
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.512
GPT teacher head0.657
Teacher spread0.145 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
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

Citations47
Published2015
Admission routes2
Has abstractyes

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