Patient-reported assessment of compassion in Spanish: a systematic review
Bibliographic record
Abstract
Aims and objectives: This systematic review aims to: (1) explore which tools have been used in Spanish to measure compassion; (2) know which of these tools could be used to assess compassion in healthcare settings from the perspective of patients; (3) evaluate the quality of these patient-reported measures in Spanish contexts; and (4) determine which of these instruments would be best suited to be used in healthcare settings. Background: Compassion has been recognized as a fundamental dimension of quality healthcare. Methods: , 2021. In accordance with PRISMA guidelines, 64 studies were included. Results and conclusions: while existing instruments, validated in Spanish, allow for the measurement of self-compassion or compassion to others, there are no valid and reliable measures currently available in Spanish to measure patient-reported compassion. Relevance to clinical practice: In order to ensure and promote compassion in the health care context, it is essential to have a valid and reliable tool to measure this construct in a patient-informed way, and this is currently not possible in the Spanish-speaking context because of the lack of such an instrument in Spanish.
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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".