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Record W2990244690 · doi:10.1177/1609406919891292

Using Participant Observation to Enable Critical Understandings of Disability in Later Life: An Illustration Conducted With Older Adults With Low Vision

2019· article· en· W2990244690 on OpenAlexaff
Colleen McGrath, Debbie Laliberté Rudman

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2019
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsWestern University
Fundersnot available
KeywordsParticipant observationPsychologyEthnographyGrounded theoryEveryday lifeFocus groupDevelopmental psychologyQualitative researchData collectionGerontologySociologyMedicineSocial science

Abstract

fetched live from OpenAlex

Research with older adults aging with vision loss has typically been informed by a biomedical theoretical framework. With a growing focus, however, on critical disability perspectives, which locates disability within the environment, new methods of data collection, such as participant observation, are needed. This article, which reports on the findings from a critical ethnographic study conducted with older adults with age-related vision loss (ARVL), aims to share those insights gained through participant observation and to demonstrate the utility of this method. Three insights were gained including the adaptive strategies tacitly employed to navigate the physical environment, a grounded understanding of social interactions that transpire in everyday contexts, and negating the presence of older adults with ARVL when accompanied by a perceived caregiver. The study findings unpack how participant observation can be used to understand social constructions of disability and gain a holistic understanding of environmental influences on the disability experience of older adults with ARVL.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.662
GPT teacher head0.653
Teacher spread0.009 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations19
Published2019
Admission routes1
Has abstractyes

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