MétaCan
Menu
Back to cohort
Record W2316451636 · doi:10.1177/008124631004000302

Sources of Stress and Support among Rural-Based First-Year University Students: An Exploratory Study

2010· article· en· W2316451636 on OpenAlexaboutno aff
Anthony L. Pillay, Humphrey Siphiwe B. Ngcobo

Bibliographic record

VenueSouth African Journal of Psychology · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySocioeconomic statusQuarter (Canadian coin)StressorAccommodationContext (archaeology)Exploratory researchInstitutionDrop outSocial psychologyClinical psychologyDevelopmental psychologyDemographyPopulationSociology

Abstract

fetched live from OpenAlex

First-year university students are faced with numerous challenges, some of which prove more than they can cope with. As a result their prospects of graduating are reduced, as reflected in the high failure and drop-out rates nationally. These challenges are certainly greater in rural-based institutions, and the authors sought, therefore, to examine the stressors endured by students from mainly poorer, rural communities attending such an institution. Fear of failing, finance and accommodation problems featured very strongly, with deaths of family members and significant others also prominent. Parents and friends were viewed as most supportive. Significantly more female than male students found friends and religious leaders/priests to be supportive. Approximately one-quarter of the sample found their siblings and health professionals unsupportive. Students younger than 21 years were more affected by conflict with and between parents than students over 21 years. The results are discussed within the context of socioeconomic as well as gender and developmental variables.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.392
Teacher spread0.358 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations96
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueSouth African Journal of PsychologySame topicHealth, psychology, and well-beingFrench-language works237,207