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Record W2773988465 · doi:10.1186/s13104-017-2979-9

Perceived causes of stress among a group of western Canadian dental students

2017· article· en· W2773988465 on OpenAlexaffabout
Alyssa Hayes, Jay Hoover, Chandima Karunanayake, Gerald Stephen Uswak

Bibliographic record

VenueBMC Research Notes · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineStress (linguistics)Family medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The demanding nature of dental education, both academically and clinically, results in higher levels of perceived stress among its students. The aim of this study was to determine how dental students at the College of Dentistry, University of Saskatchewan perceived stress. During the 2013-2014 academic year, all students were asked to complete a modified dental environmental survey (DES). RESULTS: Of the 111 students enrolled at the College that year 92 completed the survey (response rate = 83%). In general, female students reported higher stress levels than males. Higher stress levels were associated with living away from home, concerns about manual dexterity and the transition from pre-clinical to clinical studies. Additionally, students who enter dental school with higher debt loads (> 100,000) report high stress levels relating to finances. This study found that financial and clinical workloads result in high stress levels among dental students.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.316
GPT teacher head0.572
Teacher spread0.256 · 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 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

Citations58
Published2017
Admission routes2
Has abstractyes

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