A comprehensive search string informed by an operational definition of complementary, alternative, and integrative medicine for systematic bibliographic database search strategies
Bibliographic record
Abstract
BACKGROUND: Determining which therapies fall under the umbrella of complementary, alternative, and/or integrative medicine (CAIM) is difficult for several reasons. An operational definition is dynamic, and changes depending on both historical time period and geographical location, with many countries integrating or considering their traditional system(s) of medicine as conventional care. We have previously reported the first operational definition of CAIM informed by a systematic search. In the present study, we have developed a comprehensive search string informed by an operational definition of CAIM for systematic bibliographic database search strategies. METHODS: We developed a single search string for the most common bibliographic databases, including those searchable on the OVID platform (e.g., MEDLINE, EMBASE, PsycINFO, AMED), the EBSCO platform (e.g., ERIC, CINAHL), Scopus, and Web of Science, using the finalised operational definition of CAIM's 604 therapies. We searched the Therapeutic Research Center's "Natural Medicines" database for all 604 therapies, and each item's scientific name and/or synonym was included as a keyword or phrase in the search string. RESULTS: This developed search string provides a standardised list of CAIM terms (i.e., keywords and phrases) that may be searched on bibliographic databases including those found on the OVID platform (e.g., MEDLINE, EMBASE, PsycINFO, AMED), the EBSCO platform (e.g., ERIC, CINAHL), Scopus, and Web of Science. CONCLUSION: Researchers can select relevant terms for their CAIM study and insert the keywords/phrases into these databases to receive all accessible data. This search technique can simply be copied and pasted into the search bar of each database to identify research by keywords, which is the most inclusive, or by words in the article title, which is more selective. Given its versatility across multiple commonly used academic platforms/databases, it is expected that this search string will be of great value to those conducting research on CAIM topics involving systematic search strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".