Perceptions, Awareness, and Attitudes of Primary Health Care Workers Towards Management of Type-2 Diabetes in Jordan: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore health care workers’ perceptions, awareness and attitudes towards the factors that contribute to poor glycemic control among patients with Type-2 diabetes in Jordan. MATERIAL & METHODS: A qualitative study was conducted to seek rich, in-depth data about health care workers’ perceptions and attitudes regarding Type-2 diabetes. Ten semi-structured interviews were conducted with primary health care workers: four nurses, physician, dietitian, pharmacist, lab technician, dentist and receptionist who were recruited using purposive sampling. All interviews were audio recorded, transcribed and uploaded to the qualitative data analysis software package NVivo 12. Thematic analysis was used to analyze the data. RESULTS: Four themes were derived from the interview analysis: lack of knowledge of patients regarding diabetes and its management; influence of socio-cultural factors on patients’ diabetes management behaviors included eating pattern and physical activity; challenges related to the health care system including medication insecurity and health insurance policies and challenges related to staff shortages and poor patient-staff communication. CONCLUSION: The findings suggest that medical and nursing practice could usefully move away from a task-oriented medical model towards a more holistic approach in diabetes management. Health promotion should be delivered in a wider context to target the population in different age groups to raise awareness of diabetes amongst the general population in Jordan. Nurses’ skills and knowledge could be utilized to rectify patients’ misconceptions and beliefs regarding diabetes treatment.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".