Knowledge, beliefs, and concerns about bone health from a systematic review and metasynthesis of qualitative studies
Bibliographic record
Abstract
BACKGROUND: Patients with low bone density or osteoporosis need information for effective prevention or disease management, respectively. However, patients may not be getting enough information from their primary care providers or other sources. Inadequate disease information leaves patients ill-informed and creates misconceptions and unnecessary concerns about the disease. OBJECTIVE: We systematically reviewed and synthesized the available literature to determine patient knowledge, beliefs, and concerns about osteoporosis and identify potential gaps in knowledge. METHODS: A systematic search was conducted for full-text qualitative studies addressing understanding, literacy, and/or perceptions about osteoporosis and its management, using Medline, EMBASE, Web of Science, Cochrane Library, CINAHL, ERIC, PsychINFO, Psyc Behav Sci Collec, and PubMed, from inception through September 2016. Studies were selected by two reviewers, assessed for quality, and themes extracted using the Joanna Briggs Institute data extraction tool. Thematic analysis was used to identify themes and subthemes. RESULTS: Twenty-five studies with a total of 757 participants (including 105 men) were selected for analysis out of 1031 unique citations. Selected studies were from Australia, Canada, Denmark, Norway, the United Kingdom, and the United States. Four main themes emerged: inadequate knowledge, beliefs and misconceptions, concerns about osteoporosis, and lack of information from health care providers. Participants had inadequate knowledge about osteoporosis and were particularly uninformed about risk factors, causes, treatment, and prevention. Areas of concern for participants included diagnosis, medication side effects, and inadequate information from primary care providers. CONCLUSION: Although there was general awareness of osteoporosis, many misconceptions and concerns were evident. Education on bone health needs to reinforce areas of knowledge and address deficits, misconceptions, and concerns.
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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 | 0.082 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".