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Using Focus Group Research to Assess Health Education Needs of Pre-service and In-service Teachers

2009· article· en· W1963612262 on OpenAlexaffabout
Sandra Vamos, Mingming Zhou

Bibliographic record

VenueAmerican Journal of Health Education · 2009
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFocus groupIntrapersonal communicationCurriculumMedical educationHealth educationSchool health educationPsychologyHealth promotionInterpersonal communicationCommunity healthMedicinePublic healthNursingPedagogySociology

Abstract

fetched live from OpenAlex

Abstract Background: Few studies have been conducted in Canada to investigate the roles, actions and beliefs of health teachers in school health programs. Purpose: The purpose of this study was to explore health education teaching and assess related needs among pre-service and in-service teachers in a British Columbia K-12 school system, and to elicit conclusions regarding how to improve health through schools. Methods: K-12 teachers from the participating school district (N = 16) and pre-service teachers from the participating university (N = 14) participated in four focus groups. Results: Guided by the ecological model, seven themes were identified and categorized: (1) Intrapersonal Level (teaching strategies; knowledge/skills; comfort); (2) Interpersonal Level (teaching barriers); and (3) Community Level (health curricula; health programs; role of school). Discussion: Seven themes highlight the issues of school health programs from practitioners' perspectives, which also correspond with five sources of problems of school health programs classified by the WHO Expert Committee. This study reinforced the need for initial development of health educator roles and competencies to guide actions in school health improvement. Translation to Health Education Practice: The identified sources of problems illustrate the potential role that a health-promoting school approach plays to build school-community connectedness.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
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 head0.231
GPT teacher head0.579
Teacher spread0.348 · 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 designOther design
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

Citations27
Published2009
Admission routes2
Has abstractyes

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