Dolor crónico, emoción y afrontamiento
Bibliographic record
Abstract
Numerosas investigaciones muestran que las estrategias de afrontamiento juegan un importante papel para explicar las diferencias individuales en el malestar emocional experimentado por los pacientes con dolor cronico. Sin embargo, algunos trabajos recientes postulan que el afrontamiento esta influido por el malestar emocional, concretamente, proponen que las emociones negativas interfieren con un afrontamiento eficaz. El objetivo de este trabajo es contrastar estos dos modelos alternativos de las relaciones entre emocion negativa, afrontamiento y dolor cronico. En el Modelo 1 la emocion negativa y el dolor cronico se consideran consecuentes del afrontamiento y en el Modelo 2 la emocion negativa se contempla como antecedente que influye sobre el afrontamiento. La muestra estaba compuesta por 112 pacientes con dolor cronico de origen benigno que acudia por primera vez a recibir tratamiento a la Unidad del Dolor de la Residencia Carlos Haya (Malaga, Espana). Se aplicaron el ″Vanderbilt Pain Management Inventory″ (VPMI), el ″Cuestionario de Dolor McGill″ (MPQ) y el ″Perfil de Estados de Animo″ (POMS). Los resultados obtenidos a traves de un analisis de ecuaciones estructurales mediante el programa LISREL 8.20 indican que el Modelo 1 presenta un ajuste mejor que el Modelo 2. Palabras Clave: emocion, afrontamiento, dolor cronico, afrontamiento pasivo.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".