Case management for acquired brain injury: A systematic review of the evidence base.
Bibliographic record
Abstract
Abstract Background Brain Injury Case management involves coordinating and organising services and systems around brain injured clients and their families. Case managers may come from diverse professional backgrounds but have expert understanding of the unique and often complex needs of people with acquired brain injury. In what is the largest review on this topic to date, we sought to explore the international evidence base in relation to case managers and acquired brain injury. Methods Searches were conducted with seven databases, using a search strategy based on key terms relating to “case management” and “brain injury”. Eligible studies included peer reviewed publications, with books and magazine pieces excluded. All studies were screened and independently extracted by two reviewers. The quality of empirical studies was assessed by the Mixed Methods Assessment Tool (MMAT), with the appropriate Joanna Briggs Institute (JBI) checklists used for systematic reviews and text and opinion studies. Given the diversity of the evidence reviewed, findings were presented narratively. Results Electronic searches identified 2,062 studies, of which 35 were deemed eligible to be included in this review. Of these, 3 were reviews, 12 were opinion pieces, and 20 were empirical research studies. The majority came from the USA (n = 15) and the UK (n = 10), with other studies originating from Australia (n = 8) and Canada (n = 2). Narrative synthesis of included studies highlighted the challenges of measuring and evaluating the impact of case management for brain injury, vital aspects of case management, such as good communication and relationships with clients, supporting the wider family, and expertise relating to brain injury. Findings also indicated that case managers must work across the continuum of care, working with their clients’ long term, possibly across acute, to post-acute and into community services. Conclusions The relative infancy of the case management profession creates certain challenges, however it also provides an ideal opportunity to shape its future in a way that is beneficial for the client, family and healthcare providers. Case managers have already made significant developments to the profession since its conception, however, future progression requires collaboration between academics, clinicians and case managers, to produce better outcomes for clients and their families.
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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 | 0.024 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".