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Record W2032251881 · doi:10.2182/cjot.06.004

Health Education for People with Macular Degeneration: Learning Experiences and the Effect on Daily Occupations

2006· article· en· W2032251881 on OpenAlexvenueno aff
Kajsa Eklund, Synneve Dahlin‐Ivanoff

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2006
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMacular degenerationIntervention (counseling)Psychological interventionToolboxHealth educationPsychologyOccupational therapyMedical educationProcess (computing)GerontologyMedicineApplied psychologyPhysical therapyNursingComputer sciencePublic health

Abstract

fetched live from OpenAlex

BACKGROUND: Discovering New Ways, a program for people with macular degeneration, was developed based on a health education model incorporating occupation. PURPOSE: The purpose of this study was to evaluate the participants' learning experience in the program and the significance for their daily occupations. METHOD: Within an experimental design format, a content analysis was performed. Ninety-two people with macular degeneration were interviewed 1 and 16 months after the intervention. RESULTS: Participants in the individual intervention program stated that they mastered occupational tasks as a result of the provision of assistive devices. Participants in the health education program stated that the problem-solving toolbox provided them with hope and confidence to master daily occupations. The participants'experience of learning within the health education program has provided important feedback regarding the structure of the health education model and its learning process. PRACTICE IMPLICATIONS: The health education model may be seen as a model to design early interventions for individuals with health conditions which can impact occupational 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.101
GPT teacher head0.466
Teacher spread0.366 · 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 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

Citations13
Published2006
Admission routes1
Has abstractyes

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