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Record W2895982381 · doi:10.5014/ajot.2018.028407

Expanding the Occupational Therapy Role to Support Transitions From Work to Retirement for People With Progressive Health Conditions

2018· article· en· W2895982381 on OpenAlexaff
Cara L. Brown

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOccupational therapyWork (physics)Quality of life (healthcare)RehabilitationPsychologyGerontologyMedicineNursingPsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

Occupational therapists have an established and important role in helping people work while living with an illness or a disability. Although workplace accommodations and rehabilitation efforts can extend paid work for workers with progressive health conditions, the reality is that these populations often cease work earlier in the life trajectory than expected. Evidence suggests that transitioning out of paid work is difficult for people with disabilities. For example, factors such as poor health, low income, and involuntary retirement put people with multiple sclerosis at risk for poor adjustment. Given society's emphasis on paid work, the transition to unpaid work has received little attention. Occupational therapy practitioners are well positioned to contribute to enhancing the quality of life of people in work-cessation transitions who are not of traditional retirement age.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.003

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 head0.216
GPT teacher head0.493
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2018
Admission routes1
Has abstractyes

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