Programme de Creative Forces pour les familles militaires : vignettes brèves de l’art-thérapie, de la thérapie par la danse/le mouvement et de la musicothérapie
Bibliographic record
Abstract
Creative arts therapists (art therapists, dance/movement therapists, and music therapists) administer assessments and interventions that support the holistic well-being of military families affected by traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD). Clinical examples illustrate methods used by creative arts therapists to address the neurological, physiological, and psychosocial needs of service members and their families. Creative arts therapists describe areas of need, identify common goals, and present creative arts therapy interventions used with military families. Three vignettes detail the application of creative arts therapy interventions with families, couples, and parent/ child dyads. Art therapy, dance/movement therapy, and music therapy interventions were applied in discipline-specific sessions to promote familial bonding. As a result of these sessions, families were better able to identify challenges and discover strengths, improve intra-familial interactions, and create deeper mutual understanding and connectedness, all of which strengthened family resilience and encouraged motivation in other areas of rehabilitation. Creative arts therapies are an integral part of interdisciplinary care to address behavioural and rehabilitative conditions of military families impacted by TBI and PTSD. Future research should examine the efficacy of creative arts therapies in improving resilience in military 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".