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Record W6990919989

Évaluation d'un programme de valorisation de fauteuils roulants

2003· article· en· W6990919989 on OpenAlexaboutno aff

Bibliographic record

VenueHispana · 2003
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationProcess (computing)Work (physics)Data collectionWheelchairQualitative analysisField researchQualitative researchQualitative property
DOInot available

Abstract

fetched live from OpenAlex

In 1998, 11.8 % of the Quebec population over 15 years showed mobility problems and 2.3% of that group revealed that their needs were not met. The same year, the Régie de l¿assurance-maladie du Québec distributed more than 4 500 wheelchairs and repaired some 30 000 others, at a cost of over $20 million. The recycling of wheelchairs is seen as a solution for improving this situation. This paper presents an evaluation of a wheelchair recycling program. Methods.Three groups of participants involved in the recycling of wheelchairs contributed to the gathering of information. These were: personnel (n=9), occupational therapists in the community (n=5) and users of refurbished wheelchairs (n=20). Results. A participative and qualitative research approach was conducted with the 1st group. The results outline the inefficacy of the process on the structural level (e.g. not enough resources to collect unused wheelchairs), operational level (e.g. absence of norms to recycle), strategic level (e.g. absence of policy to encourage people to give back their unused wheelchair) and systemic level (e.g. the state is not imputable). A quantitative approach with the 2nd and 3rd groups revealed high satisfaction with regard to the efficacy, appearance, safety, durability and comfort as well as the delivery and follow-up services rendered. Practice Implications. The evaluation procedure herein proposed can be customized to fit other contexts and provides policy-makers with quick access to field data to help them choose the appropriate course of action.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.074
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.166
GPT teacher head0.430
Teacher spread0.264 · 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
Published2003
Admission routes1
Has abstractyes

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