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Adaptation and coping following a first stroke: a qualitative analysis of a phenomenological orientation

2006· article· en· W2074119969 on OpenAlexaff
Annie Rochette, Denise St‐Cyr Tribble, Johanne Desrosiers, Gina Bravo, Annick Bourget

Bibliographic record

VenueInternational Journal of Rehabilitation Research · 2006
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de SherbrookeUniversité de Montréal
Fundersnot available
KeywordsCoping (psychology)PsychologyFeelingQualitative researchSocial psychologyCognitive appraisalInterpretative phenomenological analysisContent analysisPsychological adaptationApplied psychologyClinical psychologySociology

Abstract

fetched live from OpenAlex

Stroke can have consequences in all areas of a person's life. If not coped with optimally, this life event will have a deleterious effect on the quality of life. The aim of this study was to improve understanding of appraisal and coping, post-stroke. Ten individuals were purposely recruited upon admission for a first stroke to participate in this qualitative study. Participants were asked to share their personal experiences with regard to their efforts to deal with the consequences of the stroke. In-depth interviews were transcribed verbatim and the content was analyzed using a rigorous method, inspired by a phenomenological orientation. Seven themes related to appraisal (unpredictability, overwhelming, feeling out of control, threat, turning point, acceptance/resignation and future prospects) and five themes related to coping (active and passive compensation, escape, change how the situation is perceived and utilization of resources) emerged from the content analysis of the in-depth interviews. In conclusion, since returning to the previous life style and activities is rather improbable, changing how the situation is perceived appears to be the most effective way of coping, in order to reach a state of acceptance/resignation favourable to an optimal quality of life.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.071
GPT teacher head0.454
Teacher spread0.383 · 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 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

Citations57
Published2006
Admission routes1
Has abstractyes

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