2018 ASSBI 41st Annual Conference Abstracts
Bibliographic record
Abstract
B ackground and aims: The aim was to review the empirical literature to determine the nature and breadth of research into the working alliance (WA) in acquired brain injury (ABI) rehabilitation. Method: A scoping review was conducted, beginning with a systematic search of relevant databases using key search terms. Studies with a focus on the role of the WA in shaping rehabilitation outcomes, and factors influencing perceptions of the WA were included and key information extracted. Results: A total of 10 quantitative studies met inclusion criteria. In most studies, ratings of the WA were compared with other process variables or outcome measures. The WA was linked to positive activity and participation outcomes, including return to work, school and driving. Client-related factors, such as age, level of education and approach to rehabilitation tasks were associated with client and therapist perceptions of the WA. Conclusions: The WA emerged as a complex process that interacts with many factors and processes at play in the rehabilitation environment. Notwithstanding the limitations of the research base, findings indicate that enhancement of the WA may indeed influence rehabilitation outcomes. Allowing time for the development of the WA, and consideration of factors, such as therapist skill, may support therapists to strengthen their alliances in ABI rehabilitation.
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.493 | 0.246 |
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".