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Record W2895769953 · doi:10.3148/cjdpr-2018-003

A Qualitative Study Exploring the Experience of the Male Dietitian from Student to Professional

2018· article· en· W2895769953 on OpenAlexaffvenueabout
Brandon Gheller, Phillip Joy, Daphne Lordly

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsMedical educationQualitative researchMedicinePsychologySociology

Abstract

fetched live from OpenAlex

PURPOSE: In Canada, dietitians serve a sex-diverse population despite the profession being predominately female (>95%). It is unknown why there are so few male dietitians. The objective of the present study was to explore the experience of the male dietitian, as a minority, in female-dominated dietetics. METHODS: Two semi-structured interviews were conducted, approximately 6 weeks apart, with Nova Scotian male dietitians. The interviews prompted participants to reflect on their experience of being a male dietitian. Interviews were analyzed using Interpretative Phenomenological Analysis. RESULTS: Male dietitians with between 1 and 17 years of experience participated (n = 6). Participant experiences were expressed as 4 themes: (i) feelings of difference and otherness, (ii) adapting to the female-dominated culture, (iii) constructing a professional identity, and (iv) passion as the driver for success. A theoretical framework for understanding the male dietitian's experience was outlined. CONCLUSION: The experience of the male dietitian is unique and is a consequence of training and practicing in a female-dominated space. The effect of adaptation and construction of a professional identity that is a response to female-dominated cultural norms is wide ranging and may be constraining for male practitioners thereby affecting their contributions to the field.

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)0.006
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.397
GPT teacher head0.606
Teacher spread0.209 · 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

Citations11
Published2018
Admission routes3
Has abstractyes

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