Poor knowledge about osteoporosis in learned Indian women.
Bibliographic record
Abstract
OBJECTIVES: The present study was done to assess knowledge about osteoporosis in learned Indian women, identify their source of knowledge and to study the correlation of level of knowledge with other variables. METHODS: A total of 73 female staff members (average age 44.7 years) of a teaching institute completed the Osteoporosis Questionnaire (OPQ). The mean +/- SD of total score for the sample was 4.1 +/- 4.1 (range -8 to 15; maximum possible score 20). RESULTS: The correct definition of osteoporosis was given by 74%, but there was general lack of awareness in all the areas assessed. There was statistically significant difference in the total score depending on the faculty of education, with staff members from the science faculty having the maximum mean score (p < 0.05). We found no influence of age, menopausal status, previous history of fracture and family history of osteoporosis on the level of knowledge. Media (74%) was the commonest source of knowledge followed by friends (49%) and doctors (25%). CONCLUSIONS: This study highlights the general lack of knowledge about osteoporosis in learned Indian women and also the need for increased involvement of medical professionals in patient education.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".