Prevalence and Perceived Effectiveness of Pharmaceutical Digital Marketing among Community Pharmacies in Saudi Arabia: A Cross-Sectional Questionnaire-Based Survey
Bibliographic record
Abstract
This research analyzes the direct-to-consumer digital marketing technologies in terms of prevalence and effectiveness. A cross-sectional study design was carried out using the non-repeated random sampling technique. Standardized questionnaires were administered by means of face-to-face interviews or online via web software Sphinx (Python Documentation Generator). The relative importance of prevalence (RIP) and the mean evaluation of effectiveness (MEE) were determined for all studied digital media and for all the different groups of respondents (pharmaceutical sales representatives, community pharmacists, consumers, and the entire sample). Inter-individual differences in RIP and MEE were assessed by computing the coefficient of variation, whereas inter-group differences were determined by one-way analysis of variance (ANOVA) with the Scheffé test as a post-hoc test. Research findings showed that, according to the opinion of all respondents, pharmaceutical promotional tools were more prevalent on healthcare websites. However, all respondents considered social media networks and chat messengers to be the most effective in terms of marketing communication. In conclusion, the results of the present research enable a better understanding of which digital platforms are more often used as media for direct-to-consumer pharmaceutical promotion, and which ones are perceived as the most effective for marketing communication.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".