MétaCan
Menu
Back to cohort
Record W2789918540 · doi:10.4236/jss.2018.63009

The Development and Validation of a Questionnaire Measuring Barriers to Career Progression Faced by Women Dentists in Saudi Arabia

2018· article· en· W2789918540 on OpenAlexaff
Mona Rajeh, Belinda Nicolua, Akram Qutob, Pierre Pluye, Shahrokh Esfandiari

Bibliographic record

VenueOpen Journal of Social Sciences · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCronbach's alphaExploratory factor analysisConstruct validityReliability (semiconductor)PsychologyMedical educationTest (biology)Psychometric testingFamily medicineMedicineApplied psychologyClinical psychologyPsychometrics

Abstract

fetched live from OpenAlex

To develop and validate a questionnaire assessing barriers faced by Saudi Arabian female dentists in the progression of their career. We developed a three-part questionnaire based on a literature review, semi-structured interviews, and consultation with 3 panels of experts. The instrument was sent to a convenience sample of 150 female dentists who were faculty members at different university hospitals in Saudi Arabia. Cronbach’s alpha was used to test reliability. Exploratory factor analysis was used to evaluate construct validity. A total of 62 dentists returned the questionnaire (response rate 41.3%), 55 of which were useable for the pilot testing of the measure. The final instrument included 20 items divided into four subscales: Family Challenges, Environment Challenges, Interpersonal Challenges, and Sociocultural Challenges. Cronbach’s α for the total questionnaire was 0.899. Exploratory factor analysis on the questionnaire led to a set of subscales differing from those defined beforehand (KMO = 0.784 very good). A new valid and reliable questionnaire has been developed that measures barriers to female dentists’ career progression in Saudi Arabia.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.129
GPT teacher head0.517
Teacher spread0.388 · 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 designObservational
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

Explore more

Same venueOpen Journal of Social SciencesSame topicDental Education, Practice, ResearchFrench-language works237,207