Bibliographic record
Abstract
Suicide risk is often reflective and nonverbal, transmitted through the affective field of the clinician-patient relationship. This discussion paper advances a defense-informed framework showing how splitting, projection, and denial may be enacted interpersonally and registered as countertransference-guilt, detachment, or affective "whiplash"-that signals unspoken suicidal disintegration. Integrating psychodynamic and intersubjective theory with emerging suicidology (e.g. Suicide Crisis Syndrome), the approach formalizes countertransference as clinical attunement rather than interference. It augments standardized assessment by adding relational and embodied data, particularly when communication is fragmented, symbolic, or defended. Practice implications include routine affect check-ins, reflective supervision, and deliberate use of relational cues in formulation and safety planning. Although examples derive from youth and high-acuity services, the framework is transdiagnostic and portable across inpatient, community, and emergency settings. The aim is a more responsive, person-centered model of suicide prevention grounded in containment, co-regulation, and therapeutic presence.
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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".