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Creating a ‘Health Promoting Curriculum’ to inform the development of a Health Promoting University: a case study

2010· article· en· W2068677962 on OpenAlexfundno aff
Margaret Coffey, Anne Coufopoulos

Bibliographic record

VenueInternational Journal of Health Promotion and Education · 2010
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsnot available
FundersLiverpool Hope UniversityUniversity of Ottawa
KeywordsCurriculumHealth educationPsychologyMedical educationMedicineSociologyEnvironmental healthNursingPedagogyPublic health

Abstract

fetched live from OpenAlex

Abstract Objective: Final year undergraduate students, undertaking a health promotion module, were asked to reflect on their experiences of contributing to the strategic development of a health promoting university. As part of this module students were engaged in carrying out a health needs assessment (HNA) in order to inform the development of a health promoting university and enhance the curriculum. Design: This case study was conducted with forty students, and used un-moderated focus groups (n = 9) where students recorded their experiences of carrying out a HNA. Results: Key findings of the students' reflections (rather than of the health needs assessment) are presented in this paper. Students reported that by carrying out a HNA they: developed an understanding of the links between theory and practice; developed communication and networking skills; found the assessment meaningful; welcomed the opportunity to make a difference; could identify improvements that could have been made, along with challenges to carrying out their work and health gains. Conclusion: This study has shown how the student learning experience has been enhanced, whilst at the same time the university (and potentially the wider community) will potentially benefit from the HNA that was carried out. Ultimately, the students were able to ‘reach the parts that other researchers can't reach’, i.e. explore complex issues amongst their fellow peers.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.078
GPT teacher head0.484
Teacher spread0.406 · 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 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

Citations6
Published2010
Admission routes1
Has abstractyes

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