Beyond Progression: Devising a New Training Model for Candidate Assessment, Advancement, and Advising at Columbia
Bibliographic record
Abstract
Research over several decades has identified significant problems with the progression model-the traditional approach to assessment and advancement of psychoanalytic candidates-including candidates' anxiety and uncertainty about the methods and fairness of their assessment, avoidance of conflictual issues with patients in order to keep cases, and reluctance to share their challenges with supervisors and advisors. In light of these findings, the Columbia Center for Psychoanalytic Training and Research restructured its psychoanalytic training programs. The progression committee, the progression advisor role, candidate application to advance through the program, and routine committee discussion of candidates were eliminated and replaced by confidential mentorship and a clear and predictable system of trainee advancement. Analytic competency-a requirement for graduation-is now determined solely from detailed written feedback regarding the candidate's achievement of the Center's learning objectives. The number of months of supervised analysis required for graduation has been reduced, as has the required length of the candidate's longest case; in addition, three-times-weekly analyses are now accepted for credit. These changes are meant to increase the transparency, objectivity, and predictability of the training experience and reduce the pressure on clinical decision making and communication between trainees and faculty. An extensive evaluation of the impact of these innovations is currently under way.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".