Bibliographic record
Abstract
The author aims to show how supportive interventions are the analyst's most relevant therapeutic means to helping patients with a feeble symbolic system transform nonsymbolic episodes and reestablish symbolic mental functioning. Symbolic and nonsymbolic modes of mental functioning are first outlined. Supportive interventions are redefined as an analyst's effort at improving a patient's nonsymbolic mental functioning, by using principally pragmatic or interactive aspects of communication to deal with her or his patient's nonsymbolic in-session experiences. These interventions are psychoanalytic when transference focused, in so far as they foster the symbolization and transformation of more primitive (nonsymbolic) layers of the transference. Some probable mechanisms underlying the effect of supportive interventions on nonsymbolic functioning include the modification of mental procedures. Supportive interventions also help restore symbolic mental elaboration through the gratification of a basic ego or self-need, bringing about a temporary relief from psychic pain, with increased affect tolerance and a renewed capacity to use symbols. This soothing effect accounts for a missing link in Bion's model of the elaborative effect of the analyst's reverie.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".