Implementation of a strengths-based approach in a traumatic brain injury community service; perspectives of community workers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".