Experiences of pre-licensure or pre-registration health professional students and their educators in working with intra-professional teams: a qualitative systematic review
Bibliographic record
Abstract
BACKGROUND: Inter-professional initiatives are prevalent in the healthcare landscape, requiring professionals to collaborate effectively to provide quality patient care. Little attention has been given to intra-professional relationships, where professionals within one disciplinary domain (such as degree and diploma nursing students) collaborate to provide care. New care models are being introduced where baccalaureate and diploma students of a particular discipline (such as nursing, occupational therapy, dentistry or physiotherapy) work closely together in teams to deliver care. Questions thus arise as to how students and educators learn to work on intra-professional teams. OBJECTIVES: To identify and synthesize evidence regarding experiences of pre-licensure health professional students and their educators on intra-professional teams and to draw recommendations to enhance policy and/or curriculum development. INCLUSION CRITERIA TYPES OF PARTICIPANTS: Pre-licensure students and educators, focusing on regulated health professions that have had more than one point of entry into practice. PHENOMENA OF INTEREST: Experiences of intra-professional team learning or teaching within various entry-to-practice categories of a particular health-related discipline. TYPES OF STUDIES: Eight qualitative studies were included in the review. Seven studies were descriptive in nature; one study was a critical analysis. SEARCH STRATEGY: A comprehensive search of various databases was conducted between June 2, 2015 and August 16, 2015, and repeated in March 2016. The search considered all studies reported and published from January 1, 2001 to March 7, 2016. Only studies published in English were included in this review. METHODOLOGICAL QUALITY: Included papers were of low-to-moderate quality; however, it is important to consider that post-positivist assumptions underpinned much of the primary research, which could explain why researcher positionality and/or influence on the research would not be addressed. DATA EXTRACTION: Data were extracted using the standardized data extraction tool from the Joanna Briggs Institute Qualitative Assessment and Review Instrument (JBI-QARI). The data extracted included descriptive details about the phenomenon of interest, populations and study methods. DATA SYNTHESIS: Research findings were pooled using the JBI-QARI. Sixty-eight findings were organized into nine categories based on similarity in meaning. RESULTS: Four synthesized findings reveal how students value intra-professional learning experiences. These experiences build positive collaborative relationships (including trust and respect); however, educator and staff attitudes and conversations create hierarchies in academic and clinical contexts resulting in tension between student groups. CONCLUSION: Despite its challenges, shared learning experiences assist students to understand each other's roles, develop communication and collaborative competencies, develop comprehensive care plans, provide more efficient care and help prepare them for their future roles as healthcare professionals. Various contextual elements could either hinder or facilitate shared learning experiences.
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 | 0.033 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".