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Enregistrement W4312445198 · doi:10.2196/36380

Supporting Midwifery Students During Clinical Practice: Results of a Systematic Scoping Review

2022· article· en· W4312445198 sur OpenAlexvenueno aff
Hafaza Amod, Sipho Wellington Mkhize

Notice bibliographique

RevueInteractive Journal of Medical Research · 2022
Typearticle
Langueen
DomaineNursing
ThématiqueNursing education and management
Établissements canadiensnon disponible
Organismes subventionnairesInyuvesi Yakwazulu-Natali
Mots-clésMentorshipThematic analysisObstetricsInclusion (mineral)Psychological interventionMedical educationCompetence (human resources)Clinical PracticeCritical appraisalQualitative researchMedicinePsychologyNursingAlternative medicineSociologySocial science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Midwifery educators are highly concerned about the quality of clinical support offered to midwifery students during clinical placement. The unpreparedness of midwifery practitioners in mentorship roles and responsibilities affects the competence levels of the next-generation midwives being produced. OBJECTIVE: The aim of this paper is to highlight various clinical support interventions to support midwifery students globally and propose a framework to guide mentorship training in South Africa. METHODS: This paper adopts a mixed methodology approach guided by the Arksey and O'Malley framework. Keywords such as midwifery students, clinical support, mentorship, preceptorship, and midwifery clinical practice were used during the literature search. The review included primary quantitative, qualitative, and mixed methods design papers published between 2010 and 2020, and studies on clinical support interventions available to midwifery students during clinical placement. The search strategy followed a 3-stage system of title, abstract, and full-text screening using inclusion and exclusion criteria. All included papers were quality appraised with a mixed methods appraisal tool. Extracted data were analyzed and presented in themes following a thematic content analysis approach. RESULTS: The screening results attained 10 papers for data extraction. In total, 7 of the 10 (70%) studies implemented a mentorship training program, 2 (20%) used a training workshop, and 1 (10%) used an intervention guide to support midwifery students in clinical practice. Of these 10 papers, 5 were qualitative, 4 mixed methods, and 1 quantitative in approach. In total, 9 of the 10 (90%) studies were conducted in high-income countries with only 1 study done in Uganda but supported by the United Kingdom. The quality of included papers ranged between 50% and 100%, showing moderate to high appraisal results. Significant findings highlighted that the responsibility of mentorship is shared between key role players (midwifery practitioners, students, and educators) and thus a 3-fold approach to mentorship. Mentorship training and support are essential to strengthen the clinical support of midwifery students during placement. The main findings produced 2 main themes and 2 subthemes each. The main themes included strengthening partnerships and consultation; and providing mentor support through training. The 4 subthemes were: establishing stronger partnerships between nursing education institutions and clinical facilities; improving consultation between midwifery educators, practitioners, and students; the quality of clinical support depends on the training content; and the training duration and structure. Hence, the researchers proposed these subthemes in a framework to guide mentorship training. CONCLUSIONS: Mentorship training and support for midwifery practitioners will likely strengthen the quality of midwifery clinical support. A framework to guide mentorship training will encourage midwifery educators to develop and conduct mentorship training with ease. More studies using quantitative approaches in research and related to midwifery clinical support are required in African countries. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/29707.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,061
score de la tête « metaresearch » (Gemma)0,147
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Intégrité de la recherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,524
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0610,147
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,003
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,196
Tête enseignante GPT0,645
Écart entre enseignants0,449 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeRevue systématique
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2022
Routes d'admission1
Résumé présentoui

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