Examining semantics in interprofessional research: A bibliometric study
Bibliographic record
Abstract
While experts in the field provide clarity between terms such as interprofessional and multidisciplinary, the published literature may not be offering this preciseness. A bibliometric analysis was conducted on 1,148 studies that examined terms such as interprofessional, multidisciplinary, and teamwork in order to examine patterns of indexing, overlap in how terms and phrases are used by authors, and consistencies in the definitions of terminology. A small number of relevant indexing terms are available in PubMed but were not regularly applied to the studies in this subject area. Our findings indicate that relying on indexing terms to locate this body of literature will not reliably identify all relevant studies when searching the literature. Definitions for these terms were typically not offered by authors, references were not regularly provided when definitions were included, and clear distinctions between the different terms were not reliably provided, thus creating further difficulties. Poor indexing, lack of consistent definitions being used in the research literature, and some authors using phrases and terms as synonyms make it challenging for educators, scholars, and researchers to search, find, and use this body of literature.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.081 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.292 | 0.460 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".