The Impact of Brand Trust on Physician’s Prescription Decision Towards Prothrombin Complex Concentrate with a Special Reference to Octaplex®
Bibliographic record
Abstract
Management of Hemostasis is an integral role for any Intensivist in assurance of recovery of a hemorrhage patient who is often treated in an ICU, and building trust of a product segment related to such vital treatment will undoubtedly hold an utmost importance. Yet in researches and articles in current Medical Marketing setup it is found to be very limited, and in the case of Biological and Bio-Similar Marketing its almost non-existence. The purpose of this research paper is to investigate and prove the significant positive impact of Brand Trust on Physicians Prescription Decision and Moderation impact powered by the synergy of Relationship Marketing. The research consists of data which was collected via online questionnaire and captured the data required from the target sample cohort which is distributed via respective specialized academic colleges of their representation through email and filed online by the participants of quantitative research. Moreover, two of the main key opinion leaders (KOLs) were interviewed and qualitative data were summarized. The collected data were analyzed using Structural Equation Modeling (SEM) procedures to reach meaningful conclusions. Thereby the study proves the significant positive impact of Brand Trust on Physicians Prescription Decision and Moderation impact which synergized by Relationship Marketing. The study is an original contribution to the field of Marketing in Biological and Pharmaceutical Industry. The proposed relationships are based on Brand Trust, Physician’s Prescription Decision and Relationship Marketing. Furthermore, the Moderating effect of Relationship Marketing on the relationship between Brand Trust and Physician’s Prescription Decision is unique to this study.
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 | 0.004 | 0.032 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".