Characteristics, Opportunities, and Challenges of Osteopathy (COCO) in the Perceptions of Osteopaths in Germany, Austria, and Switzerland: Protocol for a Comprehensive Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Currently, the importance of osteopathy within the health care system is controversial. The training structures and the acknowledgment of the occupational profile strongly differ in the German-speaking territory. OBJECTIVE: This study aims to examine the characteristics of the osteopathic profession as well as the possibilities and challenges for osteopaths in Germany, Austria, and Switzerland. METHODS: This study adopted a mixed methods design. The research topic will be examined based on qualitative and quantitative partial studies that will be conducted in parallel as well as sequentially. By applying different research methods and sample testing and by using standardized, validated measurement methods, we expect to be able to gain new insights into the work area of osteopathy. RESULTS: In November 2018, we started the research and data collection. Currently, we are conducting the first two partial studies. The planned duration of each of the partial study is 6-9 months. The project is scheduled to be completed in 2021. CONCLUSIONS: This study will examine how osteopaths define themselves in comparison with professionals from other occupational profiles and how they describe the characteristics of their work. The identification of central issues is expected to help clarify the issues and define the profession. As such, the results might contribute to the conservation and improvement of the quality of osteopathic treatment. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/15399.
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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 | 0.029 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".