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Record W2889984794 · doi:10.18806/tesl.v35i1.1282

Academic Literacy Requirements of Health Professions Programs: Challenges for ESL Students

2018· article· en· W2889984794 on OpenAlexvenueaboutno aff
Lillie Lum, Mahmoud Alqazli, Karen Englander

Bibliographic record

VenueTESL Canada Journal · 2018
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsLiteracySocializationMedical educationPedagogyVariety (cybernetics)PsychologyMedicine

Abstract

fetched live from OpenAlex

To succeed in Canadian health professions, university education programs students must initially meet a variety of program-specific and English-language admission requirements. For non-native English-speaking (NNES) students, a major challenge can be the demonstration of profession-specific academic literacy and, in particular, adequate language competency prior to admission and throughout the program. Despite the increased numbers in the adult NNES student population in Canada, the current academic literacy requirements within these programs have received minimal research focus. This study explores thecongruency of program requirements and learning supports within three major health professions programs across six Canadian universities. The data analyzed for this qualitative study include program documents on publicly accessible websites and a focused literature review. Findings suggest that in medicine, nursing, and pharmacy programs, discipline-specific academic literacy manifests itself in a wide variety of specialized written genres, ranging from reflections to theoretical analysis. Academic literacy is essential to the socialization of new students into these specialized programs and into the professions. Suggestions are offered to enhance universities’ support of the development of academic literacy of NNES students. Pour réussir comme professionnels de la santé au Canada, les étudiants inscrits aux programmes d’enseignement universitaire doivent d’abord satisfaire à une variété de conditions d’admission relatives au programme qu’ils ont choisi et à leur connaissance de l’anglais. Pour les étudiants dont la langue maternelle n’est pas l’anglais (NNES, pour « non-native English speakers »), la nécessité de faire preuve de littératie académique dans la profession de leur choix et, en particulier, celle d’une compétence langagière suffisante avant leur admission et tout au long du programme choisi peuvent constituer un défi de taille. Bien que la population des étudiants adultes de type NNES ait augmenté au Canada, les exigences actuelles de littératie académique liées à ces programmes n’ont fait l’objet que de travaux de recherche rudimentaires. La présente étude explore la concordance entre les exigences des programmes d’études et les soutiens pédagogiques au sein de trois importants programmes de formation de professionnels de la santé dans six universités canadiennes. Les données analysées pour cette étude qualitative comprennent la documentation sur les programmes concernés disponible sur des sites Web accessibles au grand public ainsi qu’une analyse documentaire ciblée. Les constatations suggèrent que, dans les programmes d’études médicales, infirmières, et pharmaceutiques, la littératie académique liée à une discipline particulière se manifeste dans une grande variété de genres d’écriture spécialisés allant de réflexions à l’analyse théorique. La littératie académique est essentielle à l’insertion des nouveaux étudiants dans ces programmes spécialisés de même que dans les professions. Des suggestions sont offertes pour rehausser le niveau du soutien des universités au développement des étudiants NNES dans le domaine de la littératie académique.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.256
GPT teacher head0.556
Teacher spread0.301 · 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 designNot applicable
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

Citations19
Published2018
Admission routes2
Has abstractyes

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