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Record W2807544516 · doi:10.1037/rep0000209

Hope and psychological health and well-being following spinal cord injury.

2018· article· en· W2807544516 on OpenAlexaff
Hannah Brazeau, Christopher G. Davis

Bibliographic record

VenueRehabilitation Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsycINFOSpinal cord injuryRehabilitationPsychological interventionPsychologyClinical psychologyWell-beingPhysical therapyMedicinePsychiatryMEDLINESpinal cordPsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: Several studies of people with spinal cord injury (SCI) have indicated that high levels of hope are linked with better adjustment, but none has assessed the extent to which hope predicts change in adjustment over time. This study examines the effect of hope assessed within the first months post-SCI onset on changes in several indicators of well-being just prior to release from institutional care and again 13 months post-SCI. METHOD: Structured interviews were conducted with 67 adults (54 men, 13 women; Mage = 44.7 years, SD = 17.2) with SCI on average 2.6 months (Time 1), 5 months (Time 2; n = 60), and 13 months post-SCI (Time 3; n = 53) using validated instruments to assess dispositional hope, depressive symptoms, subjective well-being, self-esteem, reintegration, and pain. RESULTS: Regression analyses revealed that, of the five indicators of well-being, hope at Time 1 only significantly predicted increases in subjective well-being at Time 2. However, hope predicted increased well-being on 4 of 5 indicators at Time 3. Hope was not significantly associated with changes in self-esteem at either follow-up assessment. CONCLUSION: People with high levels of hope appear to be better able to adjust to the challenges faced once they leave the rehabilitation center. Psycho-educational interventions that promote agency and pathway thinking may lead to better longer-term adjustment. (PsycINFO Database Record

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.415
Teacher spread0.388 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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