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Record W2947296288 · doi:10.17411/jacces.v9i1.183

Understanding Risk in Daily Life of Diverse Persons with Physical and Sensory Impairments

2019· article· en· W2947296288 on OpenAlexaff
Afnen Arfaoui, Geoffrey Edwards, Ernesto Morales, Patrick Fougeyrollas

Bibliographic record

VenueUPCommons institutional repository (Universitat Politècnica de Catalunya) · 2019
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPsychologyRisk managementQualitative researchPerceptionTask (project management)Activities of daily livingRisk perceptionRisk assessmentApplied psychologyEngineeringComputer securityPsychiatryBusinessComputer scienceSociology

Abstract

fetched live from OpenAlex

Managing risk of injury in daily life is a task common to all humans. However, people with impairments face significantly greater challenges in both assessing and managing risk of injury. To find out more about how individuals with impairments understand risk, we developed a qualitative study design based on semi-structured interviews. Seven people with a broad range of impairments were recruited for the study. The interviews were analyzed and organized into a codification tree subdivided into four main sections: safety and risk management, risk situation portrayal, perceptions of safety measures and finally loss of control and strong sensations. The study revealed that the difficulties related to managing risk in day-to-day situations are much higher than for people without impairments and, indeed, are possibly under reported in the literature. The realization that risk is ever present in the daily lives of people with impairments has led us to reconsider how we move forward on the remainder of our study.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.036
GPT teacher head0.273
Teacher spread0.237 · 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 designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

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