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Record W2053076547 · doi:10.1080/02699050410001671874

Returning to productive activities: Perspectives of individuals with long-standing acquired brain injuries

2005· article· en· W2053076547 on OpenAlexafffund
Licia Petrella, Mary Ann McColl, Terry Krupa, Jane Johnston

Bibliographic record

VenueBrain Injury · 2005
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsQueen's University
FundersCanadian Occupational Therapy FoundationOntario Neurotrauma Foundation
KeywordsPhysical medicine and rehabilitationPsychologyAcquired brain injuryActivities of daily livingMedicinePhysical therapyRehabilitation

Abstract

fetched live from OpenAlex

PRIMARY OBJECTIVE: The primary objective of this study was to understand how intrinsic and extrinsic factors influence productive involvement over time. RESEARCH DESIGN: Given this relatively unexplored area of study, an interpretive research paradigm was incorporated using the grounded theory methodology. METHODS AND PROCEDURES: Six participants were recruited based on inclusion criteria. They had been living with a brain injury for an average of 14 years. The primary method of data collection was semi-structured interviews, which was supplemented by programme reports to enhance methodological triangulation. RESULTS: The results revealed that factors influencing involvement in productive activities over time were conceptually linked to learning about one's capacity. These factors involved: an opportunity to try, support and feedback from others, experimenting, and participants' appraisals of themselves. CONCLUSIONS: Recommendations for clinical practice include incorporating the postulates of the social cognitive theory in rehabilitation and moving from a deficits approach towards a strengths model of practice.

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)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0070.006
Scholarly communication0.0050.004
Open science0.0020.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.037
GPT teacher head0.349
Teacher spread0.312 · 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

Citations48
Published2005
Admission routes2
Has abstractyes

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