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Record W4312445198 · doi:10.2196/36380

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

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

Bibliographic record

VenueInteractive Journal of Medical Research · 2022
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsnot available
FundersInyuvesi Yakwazulu-Natali
KeywordsMentorshipThematic analysisObstetricsInclusion (mineral)Psychological interventionMedical educationCompetence (human resources)Clinical PracticeCritical appraisalQualitative researchMedicinePsychologyNursingAlternative medicineSociologySocial science

Abstract

fetched live from 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.061
metaresearch head score (Gemma)0.147
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0610.147
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.196
GPT teacher head0.645
Teacher spread0.449 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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