Bibliographic record
Abstract
Consultation-liaison psychiatry represents a clinical modality and a useful methodological tool to improve inpatients´ quality of care (Santos, Slonczewski & Prebianchi, 2011; Pincus, 1987). However, those professionals who have no gained the necessary coping skills to address the situations of great emotional burden that are often presented by patients with pain, are more likely to develop personal distress symptoms or burnout syndrome, which impacts negatively in the treatment (Rushton, Kaszniak & Halifax, 2013; Vidal y Benito 2012). Different researchers address the importance of empathy management as a key resource against this obstacles (Cano, Leong, Williams, Dana & Jillian, 2012; Lamm, Batson & Decety, 2007; Rushton et al. 2013; Vidal y Benito, 2012). This paper reviews works which study empathy, and analyzes its relationship with the treatment of patients with pain. Hafilax (2011) notes that the ability to empathize, the differentiation of self and experiences, and ethics are variables associated –according to their stability- to a different perspective taking, and thus, to different responses to a patient in pain. When professionals manage to address the patient's suffering as something separate from themselves, with a clear recognition that this suffering is not their own –and there is empathic resonance – they can feel able to enter the patient´s world and their disease and remain personally and professionally grounded. A case is presented.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".