Predicción de las respuestas psicológicas del duelo ante la pérdida de salud por variables de personalidad, religiosidad sociodemográficas, y existenciales
Bibliographic record
Abstract
Objetivo: estudiar la asociación y predecir las respuestas psicológicas del duelo ante la pérdida de la salud por medio de alexitimia, resiliencia, religiosidad y variables sociodemográficas, existenciales y clínicas. Método: se aplicó un cuestionario de preguntas cerradas y abiertas, la escala de respuestas psicológicas del duelo ante la pérdida de la salud, la escala mexicana de resiliencia y la escala de alexitimia de Toronto a una muestra no probabilística de 120 mujeres mexicanas tratadas de cáncer. Resultados: las variables de personalidad de alexitimia y resiliencia, las variables sociodemográficas de ingreso económico y escolaridad, la variable religiosidad, la variable existencial de sentido de la vida y las variables clínicas de efectos secundarios, información, percepción del efecto curativo y adherencia al tratamiento se asociaron con las respuestas psicológicas del duelo. Se especificó un modelo que mostró buen ajuste por Máxima Verosimilitud. En este modelo, bajo afecto negativo fue predicho por menor alexitimia, mayor apoyo familiar, mayor estructura y menores efectos secundarios del tratamiento; la actitud positiva fue predicha por mayor estructura y menor ingreso económico. Conclusión: las dos variables de personalidad estudiadas tuvieron un efecto significativo y mayor que las variables sociodemográficas, de religiosidad, existenciales y clínicas. Objective: To study the associations and predict the psychological answers to mourning when facing health deterioration caused by personality, religiosity, sociodemographic, existential, and clinical variables. Method: A questionnaire of closed and open questions, the scale of psychological answers to mourning when facing health deterioration, the Mexican scale of resilience and the scale of alexithymia from Toronto were applied to a non-probabilistic sample of 120 Mexican female cancer patients. Results: All variables were associated with the psychological answers to mourning. A model showing a good adjustment to Maximum Verisimilitude was specified. This model, under negative effects, was predicted by less alexithymia, more family support, a higher structure, and less treatment secondary effects. Positive attitude was predicted by a higher structure and a less economic income. Conclusion: The two personality variables had a significant and major effect than the sociodemographic, existential, and clinical variables and the variables of religiosity
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".