Impact of Tolerance on Attention and Perceived Pain
Bibliographic record
Abstract
Tolerance, Attention and Perceived Pain have always been seen sharing some kind of relationship between them in such a way that either increase in one variable leads to the increased intensity of other or the other variable influences the first variable leading to some or the other thing.The study aimed at exploring the relationship between Tolerance Level, Attention and Tolerance Level and Perceived Pain.Two hypotheses pertaining to the same were postulated and tested using McGill Pain Questionnaire (MPQ-SCF), The Mindful Attention Awareness Scale (MAAS), and Distress Tolerance Scale (DTS) respectively.The sample comprised of 112 young adults (age 18-25) from Dehradun, Uttarakhand.Data was gathered through convenience sampling technique of data collection.A correlational research design was used to conduct the study.The analysis on the basis of statistical values was done using Correlational Analysis.The results of the study indicated that Tolerance level influences Attention in a low positive manner.The findings pertaining to the second hypothesis stated that Tolerance level share a very low positive relationship with Perceived Pain as well.Overall, it can be inferred by the result that Tolerance level has a positive impact on both the variables (Attention and Perceived Pain) respectively which means increase in the level of tolerance would lead to increased attention and perceived pain or vice versa.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".