Chiropractic practice in military and veterans health care: The state of the literature.
Bibliographic record
Abstract
OBJECTIVE: To summarize scholarly literature that describes practice, utilization, and/or policy of chiropractic services within international active duty and/or veteran health care environments. DATA SOURCES: PubMed, the Cumulative Index to Nursing and Allied Health Literature, and the Index to Chiropractic Literature were searched from their starting dates through June 2009. REVIEW METHODS: All authors independently reviewed each of the articles to verify that each met the inclusion criteria. Citations of included papers and other pertinent findings were logged in a summary table. RESULTS: Thirteen articles were included in this study. Integration of chiropractic care into military or veteran health care systems has been described in 3 systems: the United States Department of Defense, the United States Department of Veterans Affairs, and the Canadian Forces. CONCLUSION: Chiropractic services seem to be included successfully within military and veteran health care facilities. However, there is a great need for additional written evaluation of the processes, policies, practices, and effectiveness of chiropractic services in these environments.
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 | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".