Positive psychology in rehabilitation medicine: A brief report
Bibliographic record
Abstract
BACKGROUND: The field of positive psychology has grown exponentially within the last decade. To date, however, there have been few empirical initiatives to clarify the constructs within positive psychology as they relate to rehabilitation medicine. Character strengths, and in particular resilience, following neurological trauma are clinically observable within rehabilitation settings, and greater knowledge of the way in which these factors relate to treatment variables may allow for enhanced treatment conceptualization and planning. OBJECTIVE: The goal of this study was to explore the relationships between positive psychology constructs (character strengths, resilience, and positive mood) and rehabilitation-related variables (perceptions of functional ability post-injury and beliefs about treatment) within a baseline data set, a six-month follow-up data set, and longitudinally across time points. METHODS: Pearson correlations and supplementary multiple regression analyses were conducted within and across these time points from a starting sample of thirty-nine individuals with acquired brain injury (ABI) in an outpatient rehabilitation program. RESULTS: Positive psychology constructs were related to rehabilitation-related variables within the baseline data set, within the follow-up data set, and longitudinally between baseline positive psychology variables and follow-up rehabilitation-related data. CONCLUSIONS: These preliminary findings support relationships between character strengths, resilience, and positive mood states with perceptions of functional ability and expectations of treatment, respectively, which are primary factors in treatment success and quality of life outcomes in rehabilitation medicine settings. The results suggest the need for more research in this area, with an ultimate goal of incorporating positive psychology constructs into rehabilitation conceptualization and treatment planning.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".