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Record W3170695832 · doi:10.1080/07380577.2021.2020388

Impact of Lighting Assessment and Optimization on Participation and Quality of Life in Individuals with Vision Loss

2021· article· en· W3170695832 on OpenAlexaboutno aff
Turid Borgestrand Øien, Anne Mette Jacobsen, Signe Tornøe Tødten, Tine Russotti, Peter Smaakjær, Rune Skovgaard Rasmussen

Bibliographic record

VenueOccupational Therapy In Health Care · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsnot available
FundersTrygFondenVelux Fonden
KeywordsQuality (philosophy)Quality of life (healthcare)PsychologyComputer science

Abstract

fetched live from OpenAlex

This pilot study was designed to investigate the effects of a holistic lighting intervention on the quality of life for individuals with low vision. Sixty participants (44 women; median age 69 years) with visual impairment received lighting interventions, including a home visit and consultation in a lighting lab. Assisted by low vision consultants, participants evaluated their performance using the Canadian Occupational Performance Measure (COPM) before and after the intervention. Improvements in visual functioning and quality of life were evaluated using the 39-item National Eye Institute Visual Function Questionnaire (NEI VFQ-39), the Groffman Visual Tracing Test, and the Farnsworth Dichotomous Test (D15). Following the lighting intervention, scores improved for all activities in the COPM (p < 0.01), for near activities and vision-specific role difficulties in the VFQ-39 (p < 0.05), and overall in the D15 test (p < 0.05). These results suggest the intervention provided an effective method for improving the participants’ quality of life and performance.

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)0.001
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 score0.039
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.060
GPT teacher head0.430
Teacher spread0.370 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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