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Record W2015920424 · doi:10.1515/ijnes-2012-0044

Fear and Loathing: Undergraduate Nursing Students’ Experiences of a Mandatory Course in Applied Statistics

2013· article· en· W2015920424 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2013
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsCurriculumCourse (navigation)Relevance (law)PsychologyDescriptive statisticsAnxietyMedical educationNurse educationFocus groupNursingMedicineStatisticsPedagogySociologyMathematics

Abstract

fetched live from OpenAlex

This article describes the results of a qualitative research study evaluating nursing students' experiences of a mandatory course in applied statistics, and the perceived effectiveness of teaching methods implemented during the course. Fifteen nursing students in the third year of a four-year baccalaureate program in nursing participated in focus groups before and after taking the mandatory course in statistics. The interviews were transcribed and analyzed using content analysis to reveal four major themes: (i) "one of those courses you throw out?," (ii) "numbers and terrifying equations," (iii) "first aid for statistics casualties," and (iv) "re-thinking curriculum." Overall, the data revealed that although nursing students initially enter statistics courses with considerable skepticism, fear, and anxiety, there are a number of concrete actions statistics instructors can take to reduce student fear and increase the perceived relevance of courses in statistics.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.157
GPT teacher head0.507
Teacher spread0.349 · 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