The Effectiveness of Music on Quality of Life and Anxiety Symptoms in the Post Traumatic Stress Disorder in Bustan Hospital of Ahvaz City
Bibliographic record
Abstract
The present study aims at investigating the effectiveness of music on quality of life and anxiety symptoms of the veterans with post traumatic stress disorder in Bustan hospital of Ahvaz City. 40 persons were selected by simple random sampling method from the aforementioned population. The research design was an experimental one of type pretest-posttest with control group. Quality of Life inventory SF-36 (2005) and Spielberger Anxiety inventory (2005) were used for collecting the data. After selecting groups randomly, pretest was implemented on both experiment group (20 persons) and control group (20 persons). Then, music therapy intervention was implemented on the experiment group during 20 sessions each of which with 45 minutes. At the end of subject program, both groups were given posttest. Data were analyzed by using the multivariate covariance analysis. Results showed that presenting music can affect quality of life and anxiety symptoms of the veterans with posttraumatic stress disorders and this effect remained stable after one month of follow-up. Therefore, presenting music can be effective in treating the veterans with stress disorders after the accident.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".