Bibliotherapy on Depressed University Students: A Case Study
Bibliographic record
Abstract
Bibliotherapy is an expressive therapy which uses an individual relationship to the content of books and poetry and other written words as therapy. Literature published since 1990 indicates that bibliotherapy has been employed in really every helping profession with every age group. This research aims to introduce this normal inexpensive therapy to people and to show the significance of this kind of therapy for patients who are suffering from depression. What we are going to prove in this research through a case study is that using fairytales, novels and stories in bibliotherapy can help adults to overcome their depression. To get this goal, the therapist used Beck depression inventory (Beck, 1967) to measure the degree of the patients’ depression in a clinic before and after the cure period. The amount of test-takers were 180, and 23 of them participated in the experiment. When the subjects of the research were known - who were those whose grade were more than 25 - the one-month reading process started. The materials which are used are some anecdotes, novels and stories which are carefully chosen by the therapist herself according to the patients’ needs. Then the post-test was given and almost every subject’s score was less than the pre-test. The result was analyzed by the computer software SPSS. So the result is that the amount of improvement in bibliotherapy appears to be comparable to the current treatments such as drug therapy. It is also useful as a complementary therapy to speed the recovery along with conventional therapy. And also literature helps people to have a better understanding of themselves and their surroundings. Key words : Bibliothrapy; Depression; Literary texts
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".