Intricate Correlation between Body Posture, Personality Trait and Incidence of Body Pain: A Cross-Referential Study Report
Bibliographic record
Abstract
OBJECTIVE: Occupational back pain is a disorder that commonly affects the working population, resulting in disability, health-care utilization, and a heavy socioeconomic burden. Although the etiology of occupational pain remains largely unsolved, anecdotal evidence exists for the contribution of personality and posture to long-term pain management, pointing to a direct contribution of the mind-body axis. In the current study, we have conducted an extensive evaluation into the relationships between posture and personality. METHOD: We have sampled a random population of 100 subjects (50 men and 50 women) in the age range of 13-82 years based on their personality and biomechanical profiles. All subjects were French-Canadian, living in Canada between the Québec and Sorel-Tracy areas. The Biotonix analyses and report were used on the subjects being tested in order to distinguish postural deviations. Personality was determined by using the Myers-Briggs Type Indicator questionnaire. RESULTS: We establish a correlation between ideal and kyphosis-lordosis postures and extraverted personalities. Conversely, our studies establish a correlative relationship between flat back and sway-back postures with introverted personalities. CONCLUSION: Overall, our studies establish a novel correlative relationship between personality, posture and pain.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".