Barriers to and facilitators of osteoporosis investigation and treatment among older community-dwelling women in Ontario: Application of a health services utilization conceptual framework
Bibliographic record
Abstract
Background. Osteoporosis (OP) is a major public health problem leading to fractures causing considerable morbidity and health care costs. Early identification of those at high risk for OP-related fractures is possible by measurement of bone mineral density using dual-energy X-ray absorptiometry (DXA). Primary objectives. (1) To estimate the proportion of older women being investigated for OP by DXA testing and being treated for fracture prevention (taking a bisphosphonate, calcitonin and/or raloxifene), and (2) To identify the barriers and facilitators to OP investigation and treatment using a conceptual framework of health services utilization. Methods. Community-dwelling women aged 65-90 years residing within two regions of Ontario, and able to complete the standardized telephone interview, were eligible. Data collection began in May 2003 and lasted one year. Potential correlates were grouped by type as: predisposing characteristics, enabling resources or need factors. Hierarchical entry of variables, grouped by type, was used in model building with logistic regression. Results. Of the 871 participants (72% response rate), 55% had been investigated by DXA testing and 20% were receiving treatment. Adjusting for need factors, significant inequities in access to DXA testing and OP treatment were identified. Barriers (lack of enabling resources) to DXA testing included poor regional access, lower income and having a male primary care provider. Factors facilitating DXA testing (predisposing characteristics) included higher education, younger age and having preventive health care check-ups. Most of the variation associated with OP treatment was related to need factors, primarily identified by DXA test results. In addition to inequities associated with suboptimal DXA testing, barriers to OP treatment included lack of private drug coverage and shorter duration of relationship with primary care providers. Primary language being English, however, was identified as a facilitator to OP treatment. Conclusions. Multiple level preventive health care interventions are needed to reduce inequities in OP care. These should target educating older women about OP diagnosis by DXA and effective treatment options, and providing resources to physicians to better enable them to manage OP and communicate the results of DXA testing and the need for treatment to reduce fracture risk with their patients.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".