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Record W2324886646 · doi:10.5014/ajot.2016.020198

Loss of Financial Management Independence After Brain Injury: Survivors’ Experiences

2016· article· en· W2324886646 on OpenAlexafffund
Kathryn R. Koller, Lindsay Woods, Lisa Engel, Carolina Bottari, Deirdre Dawson, Emily Nalder

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in RehabilitationUniversity of TorontoBaycrest HospitalWest Park Healthcare CentreHealth Sciences CentreSunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsThematic analysisIndependence (probability theory)Acquired brain injuryFinancial independenceQualitative researchPsychologyControl (management)MedicinePhysical therapyFinanceRehabilitationManagementBusinessSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: This pilot study explored the experiences of brain injury survivors after a change in financial management (FM) independence. METHOD: Using a qualitative descriptive design, 6 participants with acquired brain injury were recruited from a community brain injury organization and participated in semistructured interviews. Data were analyzed using thematic analysis. RESULTS: Three themes emerged from the interviews: (1) trajectory of FM change, involving family members as key change agents; (2) current FM situation, involving FM strategies such as automatic deposits and restricted budgets; and (3) the struggle for control, in which survivors desired control while also accepting supports for FM. CONCLUSION: This study identifies some of the challenges brain injury survivors face in managing their finances and the adjustment associated with a loss of FM independence. Occupational therapists should be aware of clients' experiences when supporting them through a change in independence.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.059
GPT teacher head0.387
Teacher spread0.329 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

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