ElaboraciÓn de un instrumento que permita identificar y aliviar el sufrimiento. estudio preliminar
Bibliographic record
Abstract
Introduction: If our priority objective is to achieve the relief of the suffering it will be necessary acording with Lazarus, Folkman, Chapman, Gravin, Bayes and Labrador, to redude or eliminate things perceived like a threat and increase the perception of the control. Objective: For this, we need to make an instrument that it allows us to identify wich are the biopsicosocial symtoms that are perceiving in each moment like threating, so we can delate or reduce them, and at the same time to evaluate and to boost the resources. Method: We relief an instrument that includes the following groups of variables: subjective perception of the passing time, emotions aspects, worries, cooping strategies, adaptation perception, meaning of life, support perceived and suffering. Afterwards we gave the questionary to 73 oncologic patients that have been attended at the Oncologic Medical Service of the University Hospital La Paz, 31 men and 42 women with an average of 55,41 years old (DT= 14,54). Results: People who say they have more suffering, are the ones who have more pain (p <0,05) and the patients white less suffering and have more wellbeing are the ones who use strategies to coping whith their situation (p <0,05). Conclusions: The relieved instrument can be effective to facilitate the intervention to relief the suffering.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".