The Development and Validation of a Questionnaire Measuring Barriers to Career Progression Faced by Women Dentists in Saudi Arabia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".