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The process of transformation in rehabilitation: what does it look like?

2010· article· en· W2060600609 on OpenAlexafffund
Claire Jehanne Dubouloz, Judy King, Brenda Ashe, Barbara Paterson, Jacques Chevrier, Mirela Moldoveanu

Bibliographic record

VenueInternational Journal of Therapy and Rehabilitation · 2010
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueThompson Rivers UniversityOttawa HospitalUniversité du Québec en OutaouaisUniversity of Ottawa
FundersMcGill University
KeywordsProcess (computing)RehabilitationHealth carePsychologyTransformation (genetics)Qualitative researchMultiple Chronic ConditionsChronic diseaseNursingApplied psychologyMedicineComputer scienceSociologyFamily medicinePolitical scienceSocial science

Abstract

fetched live from OpenAlex

Introduction Educators and proponents of adult learning theory understand that adults can experience significant events in their lives that can lead to a process of transformation that challenges or changes pre-existing values, beliefs and behaviours. This process of transformation can occur at any point in an adult's life, including when learning to live with a chronic illness and disability. The purpose of this article is to introduce health care providers to the possible process of transformation during rehabilitation. Content The authors present a suggested model of the process of transformation in rehabilitation, one developed as part of a larger metasynthesis qualitative research project related to rehabilitation, physical health, chronic illness, and disability. The article proposes how this model could be helpful for health care providers when interacting with adults learning to live with chronic health conditions. Conclusions The process of transformation provides insight regarding the complexity of patients' experiences learning to live with chronic health conditions. The article concludes by identifying future directions for practice and research.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.043
GPT teacher head0.414
Teacher spread0.371 · 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 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

Citations23
Published2010
Admission routes2
Has abstractyes

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