Psychoanalytic Patients in the U.S., Canada, and Australia: I. DSM-III-R Disorders, Indications, Previous Treatment, Medications, and Length of Treatment
Bibliographic record
Abstract
To determine the demographics, DSM-III-R disorders diagnosed, indications used in recommending psychoanalysis, previous treatment histories, use of medication, and length of treatment in patients in psychoanalysis in the U.S., Canada, and Australia, a mail survey of practice was sent to every other active member of the American Psychoanalytic Association and every member of the Australian Psychoanalytical Society. This supplemented an earlier survey sent to all Ontario psychoanalysts. The response rates were 40.1 % (n = 342) for the U.S., 67.2% (n = 117) for Canada, and 73.9% (n = 51) for Australia. Respondents supplied data on 1,718 patients. The employment rate for patients increases as analysis progresses (p < .0001). The mean number of concurrent categories of disorders (Axis I, Axis II, and Disorders First Evident in Childhood) per patient at the start of treatment is 5.01 (SD = 3.66; median = 4; mode = 3). There are no statistically significant differences across countries. Mood, anxiety, sexual dysfunction, and personality disorders are most common. American Psychiatric Association / American Psychoanalytic Association peer review criteria for indicating psychoanalysis are followed for 86.5% of patients. Over 80% of patients in all three countries had undergone previous treatments prior to analysis. In the U.S., 18.2% of analysands are on concurrent psychoactive medication; in Australia, 9.6%. The mean length of analyses conducted in the U.S. is 5.7 years, in Australia 6.6, and in Canada 4.8. Psychoanalytic patients in all three countries have similar rates of DSM-III-R psychopathology, and many indications of chronicity.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".