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Record W4392185492 · doi:10.1071/ib23063

Implementation of a strengths-based approach in a traumatic brain injury community service; perspectives of community workers

2024· article· en· W4392185492 on OpenAlexaff
Pascale Simard, Samuel Turcotte, Catherine Vallée, Marie‐Ève Lamontagne

Bibliographic record

VenueBrain Impairment · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsGroup cohesivenessPsychological interventionFidelityQualitative researchPsychologyMental healthInclusion (mineral)NursingMedicineApplied psychologyEngineeringPsychiatrySocial psychologySociology

Abstract

fetched live from OpenAlex

Background The strengths-based approach (SBA) was initially developed for people living with mental health issues but may represent a promising support option for community participation of people living with a traumatic brain injury (TBI). A community-based organisation working with people living with TBI is in the process of adapting this approach to implement it in their organisation. No studies explored an SBA implementation with this population. This study explores the implementation of key components of the SBA in a community-based organisation dedicated to people living with TBI. Methods A qualitative descriptive design using semi-structured interviews (n = 10) with community workers, before and during implementation, was used. Transcripts were analysed inductively and deductively. Deductive coding was informed by the SBA fidelity scale. Results Group supervision and mobilisation of personal strengths are key SBA components that were reported as being integrated within practice. These changes led to improved team communication and cohesiveness in and across services, more structured interventions, and greater engagement of clients. No changes were reported regarding the mobilisation of environmental strengths and the provision of individual supervision. Conclusion The implementation of the SBA had positive impacts on the community-based organisation. This suggests that it is valuable to implement an adaptation of the SBA for people living with TBI.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.086
GPT teacher head0.418
Teacher spread0.332 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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