Patients' Knowledge, Attitudes, and Behaviors Toward Family Doctors When They Attend A Private Primary Care Center
Bibliographic record
Abstract
Background: Healthcare systems often face imbalances, with tertiary care centers overburdened due to insufficient utilization of primary care. Family physicians, key players in holistic and cost-effective healthcare delivery, are underutilized due to limited public awareness. This study aimed to assess the knowledge, perceptions, attitudes, and practices of patients visiting a private primary care center regarding family physicians to support the promotion of family medicine as a vital specialty. Methods: This study was conducted at a primary care center. A pre-tested questionnaire collected data from 300 patients meeting inclusion criteria. The questionnaire evaluated socio-demographic information, knowledge, perceptions, attitudes, and practices concerning family physicians. Responses were analyzed using descriptive statistics and SPSS version 19.0. A 5-point Likert scale was employed, with results condensed into three categories for simplicity. Results: Participants demonstrated high awareness (96.7%) of the role of family physicians and expressed strong confidence in their abilities to provide holistic and cost-efficient care. Positive attributes such as attentive listening, professionalism, and patient involvement in decision-making were acknowledged by over 95% of respondents. While 88% identified family physicians as their preferred initial healthcare contact, actual practice patterns revealed preferences for specialists in areas like pediatric care (66.2%), obstetrics (81.6%), and diabetes management (52%). Consistent consultations improved patient familiarity and rapport with family physicians. Conclusion: Despite favorable knowledge and attitudes, many patients opted for specialists over family physicians for specific health concerns. Further initiatives to enhance public awareness, address perception gaps, and expand the scope of family medicine are essential to strengthen its role in integrated healthcare systems.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".