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Student Stress and Academic Satisfaction: A Mixed Methods Exploratory Study

2020· article· en· W3082199570 on OpenAlexaff
Iffat Naeem, Fabiola E. Aparicio-Ting, Patti Dyjur

Bibliographic record

VenueInternational Journal of Innovative Business Strategies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyBurnoutMedical educationExploratory researchContext (archaeology)Stress (linguistics)Qualitative propertyQualitative researchApplied psychologyClinical psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

Student satisfaction has implications for student academic success, engagement, and retention within their programs. The aim of this study is to explore the relationship between three levels of student stress and satisfaction in an undergraduate program. To explore: 1) the distribution of stress and program satisfaction within the program; 2) associations between measures of student stress and program level satisfaction; and 3) student perceptions about stress and program satisfaction using qualitative data. Online questionnaires were administered to 24 students in a small, undergraduate health sciences honours program. Correlational analysis was used to fulfill the objectives, which were triangulated with qualitative data. Levels of stress were low and academic satisfaction high within this program. Qualitative results suggest a small program size and social support as explanatory factors. A negative correlation was found between burnout and overall program satisfaction. Qualitative findings indicate that program context and individual characteristics may describe this association. This study has implication in promoting adaptive personality traits within students and facilitating supportive and engaging program environments to ensure student success.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.109
GPT teacher head0.508
Teacher spread0.399 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2020
Admission routes1
Has abstractyes

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