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Record W4401960821 · doi:10.1177/00084174241262246

Essential Occupational Therapy Competencies for Low Vision and Blindness

2024· article· en· W4401960821 on OpenAlexaffvenueabout
Julia Foster, Michelle Borgal, Sarah Wise, Colleen McGrath, Rosemary Lysaght

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsOccupational therapyDelphi methodBlindnessVisual impairmentFocus groupLow visionMedical educationPsychologyMedicineDelphiQualitative researchCurriculumNursingOptometryPhysical therapyPsychiatryPedagogyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Background. The prevalence of low vision and blindness in persons across the lifespan means that occupational therapists will encounter these conditions across all areas of practice. Practitioners must be equipped with competencies necessary to recognize and respond to vision loss-related concerns. Purpose. This study sought to identify essential occupational therapy competencies when providing services to people with low vision and blindness. Method. The study employed a three-phase modified Delphi methodology administered through online surveys and focus group. Respondents included people with low vision or blindness, professionals with special expertise in low vision/blindness, and occupational therapists in other practice areas. Data were analyzed using an iterative, consensus-generating strategy involving quantitative analysis of competencies, qualitative input, and expert panel review. Findings. The process yielded a 51-item competency framework organized into six domains. Implications. The framework provides the foundation for a common curriculum for Canadian occupational therapy programs and for the creation of educational resources.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.254
GPT teacher head0.510
Teacher spread0.256 · 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 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

Citations1
Published2024
Admission routes3
Has abstractyes

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