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Record W2141355259 · doi:10.1080/14623943.2010.505712

Critical reflection and prenatal screening public education materials: a metaphoric textual analysis

2010· article· en· W2141355259 on OpenAlexafffundabout
Meredith Vanstone, Elizabeth Anne Kinsella

Bibliographic record

VenueReflective Practice · 2010
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsDirectiveMetaphorGuidelineLiteral and figurative languageCritical reflectionReflection (computer programming)PsychologyPrenatal screeningMedicinePrenatal diagnosisPedagogyMedical educationSociologyNursingPregnancyLinguisticsComputer science

Abstract

fetched live from OpenAlex

This paper presents a study of prenatal screening educational materials that uses metaphoric textual analysis to critically examine implicit messages in the educational resources. In Canada, the Clinical Practice Guideline on prenatal screening explicitly states that counselling about prenatal screening should be non‐directive, promote choice, and be respectful of the needs and quality of life of people with disabilities. This study examines whether the written public education materials available to Canadian women are consistent with these aims. Findings from the prenatal screening patient education pamphlets are presented, and prominent figurative and metaphoric language identified in the educational resources is highlighted. The discussion considers the ways in which the education pamphlets may communicate subtle messages to women, and offers considerations for the design of non‐directive prenatal screening educational materials. In addition, the discussion considers the ways in which metaphor analysis can foster critical reflection and reveal insights important for the design of educational materials in health care.

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)0.011
metaresearch head score (Gemma)0.036
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 score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0070.019
Scholarly communication0.0070.008
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.404
Teacher spread0.367 · 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

Citations9
Published2010
Admission routes3
Has abstractyes

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