Patient attitudes toward physicians
Bibliographic record
Abstract
Purpose Stakeholders affiliated with healthcare services should understand patient attitudes and criteria that are involved in selecting a personal physician. The purpose of this paper is to identify the factors that are significant to patients in selecting or deselecting physicians as providers of healthcare services. Design/methodology/approach The research structure was set to theorize the physician selection criteria (PSC) model into two phases. The first phase developed a conceptual model as revealed from healthcare consumer perceptions. The second phase was designed to test and validate the model through cause–effect statistical analysis underpinned by theoretical explanations through an empirical study. Findings Through an empirical study of benchmarking perceptions of people from 15 different countries, qualitative PSC were gathered and used to formulate an initial PSC model. Based on the proposed model, a validity test was conducted, and finally, the PSC model was developed, resulting in several interesting and self-explanatory outcomes. Research limitations/implications The model was tested in only one (relatively cosmopolitan) city. For proper generalization, it should be tested in countries with differing healthcare service systems. Practical implications The results of this study are interesting, important and have potential values to academics and medical professionals. The study provides strong evidence that a physician’s external approach to patients is the most significant issue for patients seeking medical services. This does not refer to basic medical services, but rather the treatment process, where the physician’s behavior and positive attitude has the strongest effect on the patient’s decision to choose one physician over others. Originality/value Final PSC model has identified some significant theoretical explanations for academics and professional justifications for practitioners.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".