Psychodynamic Psychiatry and Psychoanalysis in Indonesia
Bibliographic record
Abstract
Indonesia, a country with a vast population of approximately 275 million people on over 17,000 islands, currently has 1,221 psychiatrists nationwide. Psychodynamic psychiatry and psychoanalysis are integral to the practice of psychiatry in Indonesia, primarily because of the charismatic influence of Bachtiar Lubis, who trained in Canada in the early 1960s. Upon his return to Indonesia, Bachtiar Lubis supervised a generation of psychiatrists, including two of this article's authors, who carried on his pedagogical work. The psychodynamic model and treatments have faced obstacles limiting their acceptance in Indonesia, including importing a Western model that has not been culturally adapted to treat patients in the East, the stigma of mental illness in local communities, and the complex comorbidities of persons who seek psychiatric care. Psychodynamic psychotherapy in Indonesia is presently taught in university-based residency programs for eight semesters. A psychodynamic psychotherapy competency-based curriculum was adopted nationwide. The dissemination of psychotherapy knowledge and skills is greatly assisted by an active psychiatric professional association-the Indonesian Psychiatric Association Psychotherapy Section, a member society of the World Federation for Psychotherapy. The authors propose international and regional academic collaborations to maintain enthusiasm among trainees and improve quality of care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".