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Record W2950110094 · doi:10.55016/ojs/ajer.v65i2.56527

Some Factors that Influence Students’ Experiences, Engagement, and Retention in a Practical Nursing Program

2019· article· en· W2950110094 on OpenAlexaffvenueabout
Viola Manokore, Jennifer Mah, Fauziya Ali

Bibliographic record

VenueAlberta Journal of Educational Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsQuest University CanadaNorQuest College
Fundersnot available
KeywordsPsychologyFocus groupThematic analysisAttritionContext (archaeology)BelongingnessMedical educationDemographicsQualitative researchPedagogyNursingSociologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

This paper reports on qualitative data from a larger study that was designed to identify some of the factors that influence practical nursing students’ experiences, engagement, resilience, attrition, performance in the program and professional licensing examination. Some descriptive statistics were included in this paper in order to provide context of the demographics of the students in the program. We explored and identified some factors that influence practical nursing students’ experiences and attrition in the practical nursing program at a community college in the Prairie Region in Alberta. Students enrolled in the program during Winter 2015 term were invited to participate in the study. 263 students consented to participate in the study and completed an online survey on student engagement and belongingness. A total of three focus group discussions (FGDs) were conducted to gather more information on students’ experiences. Exit interviews were completed with 21 students who dropped out of the program. Thematic analysis was done on FGDs and exit interviews. The themes that emerged from the data as main factors that influenced student experiences include institutional, social, and cognitive factors. Exit interview data shows that students “depart” due to financial, academic, family, and career choice changes. Cet article fait état de données qualitatives tirées d’une plus grande étude conçue pour identifier quelques-uns des facteurs qui influencent les expériences, la participation, la résilience, le taux d’attrition et le rendement des étudiantes infirmières au sein du programme et lors de l’examen d’accréditation professionnelle. Des statistiques descriptives sont présentées de sorte à fournir un contexte démographique des étudiants dans le programme. Nous avons exploré et identifié des facteurs qui influencent les expériences et le taux d’attrition des étudiantes infirmières dans un programme de soins infirmiers auxiliaires d’un collège communautaire dans la Prairie Region en Alberta. Nous avons invité les étudiants inscrits au programme pendant le semestre d’hiver 2015 à participer à l’étude. Au total, 263 étudiants ont accepté d’y participer et ont complété une enquête en ligne portant sur la participation des étudiants et leur sentiment d’appartenance. Trois discussions ont eu lieu avec des groupes de consultation afin de recueillir davantage d’information sur les expériences des étudiants. Des entrevues de départ ont eu lieu avec 21 étudiants qui ont quitté le programme. Les discussions des groupes de consultation et les entrevues de départ ont été soumises à une analyse thématique. L’analyse a permis d’identifier des facteurs qui influencent les expériences des étudiants. Parmi ceux-ci, notons des facteurs institutionnels, sociaux et cognitifs. Les données des entrevues de départ indiquent que les étudiants quittent le programme pour des motifs financiers, académiques, familiaux ou en raison d’un changement de choix de carrière. Mots clés : sentiment d’appartenance; décrochage; soins infirmiers auxiliaires; participation des étudiants; rétention des étudiants

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)0.004
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.175
GPT teacher head0.563
Teacher spread0.388 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
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

Citations3
Published2019
Admission routes3
Has abstractyes

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