Correlates of Physical Activity and Degree of Pain among Older Adults
Bibliographic record
Abstract
Objective: To determine the degree of pain presented by the people selected in the present study and to establisha possible relation between this first variable with other socio-demographic ones (age, gender, civil status andoccupation), as well as whether or not they practice physical activity, and if so, what type of activity; To checkthe relationship between the practice and type of physical activity, with the socio-demographic factors; age,gender, civil status and occupation.Methology: 564 participants, with an average age of 61.05 years and an age range between 40 and 88 years fromthe Sabah, Malaysia, was made by a sampling technique intended to provide a natural composition with acriterion of inclusion, that is to say, people aged 40 or older. Various measuring instruments were chosen (painscale and questionnaire) to collect the variables selected.Results: The results indicated that 80.9% of the participants presented with a moderate degree of pain, thecervical area and knees being the structures most affected. On the other hand, 73.2% of the sample populationhabitually carried out physical and sporting tasks, with no differences according to gender, but showingdifferences according to age and occupation. The most common activities were walking (88.64%) and keep fit(25.65%). There was no correlation between physical activity and the degree of pain.Conclusion: The main conclusions highlight the need to create exercise and health protocols and programmeswith a multidisciplinary approach, adapted to the individual needs of each person and the promotion of theconstruction of new, modern sporting facilities in rural areas so that people may enjoy better sportingopportunities.
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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 | 0.001 | 0.002 |
| 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".