Exploring Interprofessional Education for Collaborative Practice (IPECP) in Oral Health Education for Professional and Interprofessional Socialization and Identity Development: A Scoping Review Protocol
Bibliographic record
Abstract
Introduction: Interprofessional collaboration between health professionals supports enhanced patient care and outcomes. IPECP in pre-licensure education supports professional and interprofessional socialization. Within IPECP students develop an understanding of their professional role and identity. IPECP experiences also contribute to interprofessional identity formation, where collaborative attitudes, behaviours, and skills are developed that support collaboration in practice. IPECP literature in oral health education for dentistry(DDS) and dental hygiene (DH) students is limited. It is not well understood how DDS and DH students are educated in IPECP and prepared for collaborative practice. Inclusion criteria: This review will consider studies specific to IPECP models used in the pre-licensure education of DDS and/or DH students and IPECP models used in health professions education that include at minimum one (1) cohort of DDS or DH students. Methods: A pilot search of CINAHL and DOSS was conducted to identify keywords and indexed terms. Databases searched will include CINAHL, MEDLINE, DOSS, and APA PsycInfo. Peer-reviewed articles satisfying inclusion criteria will be sourced and bibliographies searched for additional literature. Articles will be independently screened for title and abstract, followed by full-text review by two reviewers. A modified JBI-tool will be used for data extraction. Data will be presented in table and diagram forms, accompanied by a narrative summary.
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 | 0.099 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".