Bibliographic record
Abstract
abstract The therapeutic use of pets has usually involved institutional visitation. Noting Freud's comments on the discovery of the profound connection between himself and his dogs, and recent research on the regulatory functioning sustained by a libidinal pair‐bond, this paper reports the therapeutic uses of the clinician's companion animal, a Labrador retriever, in a psychoanalytically oriented private practice. Patients were sitting up and seen once or twice a week. I argue that the containment of the treatment setting, attachment theory and a number of Winnicott's concepts – the good enough mother, the True and False Self, and the development of concern – are pertinent to an understanding of this approach to treatment, and that the right companion animal may contribute to the emergence of a True Self. The companion animal has the qualities of ‘devotion’, offers expressions of love and acceptance, tactile opportunity and responsiveness. I make a distinction between a ‘corrective object relationship’ and a ‘corrective emotional experience’, and take into account analytic debates about the ‘corrective emotional experience’. Through clinical vignettes I demonstrate the value of a trained companion animal as a psychotherapeutic addition in a private practice.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".