Experiences of pre-licensure or pre-registration health professional students and their educators in working with intra-professional teams: a qualitative systematic review
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,014 | 0,015 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».