Physicians’ loyalty to branded medicines in low-middle-income countries: A structural equation modeling
Bibliographic record
Abstract
Objective As gatekeepers in health continuum, physicians play a pivotal role in persuading patients to consume a specific medicine, and their prescription behavior has a great effect on both healthcare costs and pharmaceutical markets. Taking the important role of physicians in healthcare system into account, in this study we have tried to empirically prioritize those factors that affect physicians’ loyalty behavior in prescribing branded medicines. Methods This research is grounded on a survey through which 437 specialist physicians were randomly invited to fill out the questionnaire of the survey. Structural Equation Modeling was performed to evaluate the research model and to test the research hypotheses. In addition to demographic section, six measures were used to evaluate the prescription behavior of physicians in terms of loyalty. Key findings The results revealed that there are some factors influencing physicians’ loyalty to branded medicines, among which professional influence is perceived to be the most important factor as compared with others. In contrast, the results rejected this hypothesis that promotional tools such as tangible rewards have a significant effect on physicians’ loyalty behavior. Conclusions The results contribute to the pharmaceutical companies endeavoring to develop fair, ethical, and effective marketing strategies to increase physicians’ loyalty to their products. Furthermore, by comparing the results of similar studies, this research has shed light on this fact that influencing factors on brand loyalty may be different across countries over the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".