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Record W7071871686

‘Understanding secondary HPE teachers’ professional identity and self-efficacy to teach nutrition in schools in Australia’

2019· other· en· W7071871686 on OpenAlexaboutno aff

Bibliographic record

VenueUSC Research Bank (University of the Sunshine Coast) · 2019
Typeother
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)Thematic analysisQualitative propertyProfessional developmentQualitative researchData collectionFocus group
DOInot available

Abstract

fetched live from OpenAlex

Low teacher self-efficacy has been acknowledged as a common barrier to teaching nutrition. Teachers’ professional identity interact reciprocally with their self-efficacy. Teachers who have a clear sense of their professional identity exhibit a stronger sense of self-efficacy. Likewise, teachers who have a strong sense of self-efficacy exhibit a clear sense of their professional identity. To date, the research on teachers’ professional identity tends to focus on generalist or pre-service teachers, and the development of their identities. Moreover, most findings on teachers’ self-efficacy to teach nutrition originate from Canada and the United States, leaving a gap in research within the Australian context. This research study aims to investigate the relationship between the professional identity of secondary Health and Physical Education (HPE) teachers ( five years of HPE teaching experience), and their self-efficacy to teach nutrition in schools. This study utilises an explanatory sequential mixed-methods design involving collecting and analysing quantitative and qualitative data across two phases. The first phase is built upon through the second qualitative phase to refine and explain the statistical results by exploring participants’ views in more depth. Data collection methods include an initial online survey comprising published questionnaires and opened ended questions which will provide data for clustering groups of participants using Ward’s cluster analysis method. Thematic analysis of the qualitative data and themes identified in the quantitative data from the survey will form an initial analysis of each cluster. The analysis will provide further description and present a deeper understanding of the participants within each cluster. Semi-structured interviews are then used in phase two to gather qualitative data that will further explain or elaborate on the results obtained in phase one, to provide a richer and thicker description of the participants within each cluster. The presentation at this conference will be a discussion of the preliminary findings of the data generated from the online survey in phase one of the study. The outcomes of this research will inform practicing secondary HPE teachers, teacher educators, and policy makers about the significance of developing a clear sense of HPE teachers’ professional identity and self-efficacy, as it is closely linked to their self-efficacy and experiences to teach nutrition. Also, the findings of this study will enable school communities to understand the need to support teachers’ development of their professional identity and self-efficacy. Subsequently, improvements to policies to support teachers’ development of their professional identity and self-efficacy can be enabled.

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)0.004
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.306
GPT teacher head0.492
Teacher spread0.186 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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