Putting Humpty Dumpty Together Again
Bibliographic record
Abstract
Acquired brain injury commonly results in both cognitive and emotional sequela, and it is increasingly recognized that these domains of functioning interact. Consequently, interventions directed at only or primarily one domain may be confounded by this interaction. To maximize treatment potential, we believe cognitive rehabilitation must integrate both cognitive and emotional interventions, and attend to belief systems about, and affective responses to, cognitive challenges. We review the scant literature addressing the impact of combined interventions for clients with acquired brain injury. Integrated with these reviews are 2 case studies that appear to break treatment "myths." Specifically, we address the notion that emotion-focused treatments are appropriate only for clients with awareness or insight and the notion that cognitive interventions are ineffective, and potentially even contraindicated, for clients whose profile suggests emotional distress and functional, as opposed to neurological, impairments. In each of these cases, we demonstrate that combining cognitive and emotional interventions was not only effective but also even more valuable than previous treatment approaches aimed exclusively at one domain. We conclude by emphasizing the importance of understanding emotional response to, and beliefs about, cognitive difficulties in developing effective interventions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".