FERRAMENTAS QUE PODEM SER UTILIZADAS NA AVALIAÇÃO DA EMPATIA DOS PROFISSIONAIS DE SAÚDE: REVISÃO INTEGRATIVA
Bibliographic record
Abstract
Empathy is an essential skill in healthcare practice, associated with effective communication, treatment adherence, and patient satisfaction. The measurement of empathy has gained increasing attention, leading to the development and adaptation of several psychometric tools. This study aimed to identify and describe the main instruments available for assessing empathy in health professionals, considering their applicability, assessed dimensions, and psychometric validity. An integrative literature review was conducted in the PubMed, SciELO, and LILACS databases, covering the period from 2011 to 2025, using the descriptors “Empathy,” “Health Professionals,” “Assessment,” “Measurement Instruments,” and “Humanization of Care.” Twenty-four articles were selected. The main instruments identified were the Jefferson Scale of Physician Empathy (JSPE), Interpersonal Reactivity Index (IRI), Consultation and Relational Empathy Measure (CARE), and Toronto Empathy Questionnaire (TEQ). The Empathy Quotient (EQ) also showed solid psychometric performance. A trend of empathy decline during medical training and the influence of occupational factors were observed. It is concluded that measuring empathy is essential to strengthen humanized practices and support educational and care policies in health.
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 | 0.046 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".