Attitudes of Primary Healthcare Centers Workers' towards Artificial Intelligence in Healthcare
Bibliographic record
Abstract
Objective: The appearance of artificial intelligence (AI) technologies has developed as a promising solution to develop healthcare efficiency and improve patient outcomes. The objective of the study was to assess the primary healthcare centers worker’s attitudes towards artificial intelligence in healthcare. Methods and Materials: A descriptive cross-sectional study design has been carried out from period 9 October, 2024 to 1st March, 2025. Non-probability (purposive) sample of (451) primary healthcare centers workers from (12) primary healthcare centers in Baghdad City centers, were selected to participate in the study. Data were collected through a self-report instrument that includes the socio-demographic data (age, sex, marital states, job description, educational qualification, years of experience and AI training), Likert scale of primary healthcare worker’s attitudes towards AI. The data were analyzed using the SPSS-26. Findings: The study results reveals that, more than quarter of the participant age (28.1-34) years (n=115;25.5%), The majority of the study participants didn’t participate in training course regarding artificial intelligence (96.7%). More than third of the study participants have 6-10 years’ experience in primary healthcare centers (34.6%). More than half of the study participant have positive attitudes towards artificial intelligence in healthcare (54.33%). There was a statistical significant positive correlation between the participants age, educational qualification, years of experience and the level attitudes towards artificial intelligence in healthcare (r=.208 at p <0.01: r=.396 at p<0.05: r=.136 at p =0.01) respectively. Conclusion: It can be concluded that more than half of primary healthcare centers workers have positive attitudes regarding artificial intelligence in healthcare.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".