Reflections on recovery, rehabilitation and reintegration of injured service members and veterans from a bio-psychosocial-spiritual perspective
Bibliographic record
Abstract
Medical interventions regarding trauma resuscitation have increased survivorship to levels not previously attained. Multiple examples from recent conflicts illustrate the potential return to high-level function of severely injured service members following medical and rehabilitative interventions. This review addresses the goals of rehabilitation, distills hard-won lessons of the last decade of military trauma and rehabilitation, and recommends the use of a bio-psychosocial-spiritual approach to care that can be applied at all tiers of the health care system. Questions on enabling participation in meaningful life activities include the following: Why do some patients do well and others do not? What elements contribute to positive outcomes? What factors relate to suboptimal results? Lessons learned revolve around the importance of considering the physical, psychosocial and spiritual aspects of a person’s well-being; empowering patients by fostering self-efficacy; and helping patients find meaning in life events and set high-level goals. A bio-psychosocial-spiritual model from the rehabilitation medicine literature — the Canadian Model of Occupational Performance and Engagement — is proposed as a guide to the provision of person-centred care and the maximization of a person’s functioning posttrauma.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".