Professional and Personal Development After Psychoanalytic Training: Interviews with Early Career Analysts
Bibliographic record
Abstract
After analytic training, graduates position their newly acquired identity as "psychoanalyst" in the context of their broader career, contemplating whether to start new analytic cases, adapting their new knowledge base to psychotherapy practice, and deciding how to focus their professional and personal interests going forward. Using questionnaires and interviews, the Columbia Postgraduate Analytic Practice Study (CPAPS) has prospectively tracked the career trajectory of 69 of 76 graduates (91%) from the Columbia University Center for Psychoanalytic Training and Research since 2003. In this paper grounded theory is used to identify developmental themes in interviews with analysts who have been followed for at least ten years. Recent graduates are negotiating the following challenges: developing a sense of competence, navigating relationships with colleagues and former supervisors as situations change and roles shift, transitioning into becoming mentors, and balancing the competing responsibilities of professional and personal life. Disillusionment about aspects of training, analytic practice, analysis as a treatment, institute politics, and the field in general emerges as a stark reality, despite a high level of career satisfaction. Educational recommendations include making career development opportunities available and providing a realistic view of both practice realities and expectations of analytic treatment outcome.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".