Systematic Review of Psychotherapy, Pharmacotherapy and Other Alternative Depression Treatments in Iranian University Students
Bibliographic record
Abstract
Consistent with community samples, depression is extremely prevalent among university students. Depression, with increasing incidence in the past two decades, can affect broadly on student’s health and lifestyle; so identify the various treatment of this disorder can be valuable in reducing level of depression and thereby its many other outcomes. The main aim of this study is to conduct a systematic review of conducting studies about depression intervention made in Iran by 2016. with employing systematic review process, searching, screening and qualitative evaluation, Final 40 papers of relevant randomized controlled trials are examined to determine which treatments have been found to be effective for depression in Iranian university students.40 articles related to intervention in depressed students were placed in 4 main groups, Including Psychotherapy, Pharmacotherapy, Exercise therapy and other alternative depression treatments. of 40 papers included, 24 articles deal with Psychotherapy, including cognitive-behavioral , life skill training and Poetry and Narrative therapy, 3 articles related to Pharmacotherapy, 8 articles allocated to Exercise treatments of depression and 5 paper related to other alternative depression treatments. We did not find any trial study of combined intervention or comparing the difference of various Psycho or Pharmacy treatments for depressed student. It should be noted that various treatments may vary in terms of their benefits due to the population type, sub-group interventions, and presence of particular symptoms. Thus, further research is needed for better treatment of depression.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".