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Record W7055218153

Challenges faced by program administrators when training international genetic counseling students

2018· dissertation· en· W7055218153 on OpenAlexaboutno aff

Bibliographic record

VenueCSUN ScholarWorks (California State University, Northridge) · 2018
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationGenetic counselingTraining (meteorology)Cultural diversityDiversity (politics)Cultural competenceHealth carePopulation
DOInot available

Abstract

fetched live from OpenAlex

The effect of globalization has resulted in a diverse population of patients that healthcare professionals treat today, and this has led to challenges in the fields of nursing, psychology, and other healthcare professions. Genetic Counseling is faced with similar challenges and these can be countered by increasing diversity and cultural sensitivity in the profession. One potential but not the only method is to increase the presence of international students in the genetic counseling programs. Studies in the past have looked at the issues and challenges faced by students in fields such as genetic counseling, nursing, and clinical psychology. These studies acknowledge the benefits of having international students in the training program as they increase the cultural awareness of the training program and in turn the profession. On the other hand, cultural differences and communication challenges add to the complexity of training international students in healthcare professions. This study aims to look at the previously unexplored side of the program administrators, and assess their perspectives on the training of the international genetic counseling students. Aims: 1. Identify themes that describe the experiences of program administrators and clinical supervisors while training international students.2. Develop recommendations to help program administrators and international students to prepare for these challenges and increase their success in the training program. Methods: For the purposes of this study, participants were supposed to be Program Administrators of Masters level genetic counseling programs accredited by the Accreditation Council for Genetic Counseling (ACGC) and located in the United States and Canada for at least 6 months. In the context of this study, international students are students or applicants who are not from the United States or Canada. A semi-structured interview guide was developed. All the Program administrators were contacted by sending out an email through the Association of Genetic Counseling Program Directors (AGCPD) listserv. These semi-structured interviews were transcribed verbatim and analyzed to identify emerging themes. Results and Conclusions: It was found that about 5-10% of the applicant pool applying to the genetic counseling training programs is international. The data generated from the interviews were organized into four categories: 1) Motivation to accept international students, 2) Application and admissions process, 3) Challenges during training, and 4) Experiences post-graduation. The Program administrators identified that international students take more time and effort when they are a part of the cohort. Assessing international applicants for a position in the genetic counseling programs is time-consuming. Even though the overall assessments employed for these applicants are the same as those employed for domestic students, international applicants need to be assessed on a much deeper level to ensure the success of the students and the program. Most challenges encountered during the training of these students arise due to differences in the previous education system, lack of language proficiency, unfamiliarity with the healthcare system and cultural differences. Despite facing these challenges, the overarching sentiment has been that having an international student in the classroom is rewarding and worth the time.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0260.000

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 head0.020
GPT teacher head0.287
Teacher spread0.267 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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