Bibliographic record
Abstract
In recent years, self-medication products have undergone a dramatic change due to the advent of herbal medicines, dietary supplements, nutraceuticals and health foods in addition to traditional nonprescription medicines and the increasing societal preferences towards greater individual control over the use of medicines. Globally, the role and importance of nonprescription medicines in healthcare delivery is also rapidly increasing due to the potential cost-savings. Hence, this area is beginning to receive much attention from regulatory authorities, academia and professional/industry/trade organizations. This dissertation presents a comprehensive analysis of the classification of nonprescription medicines and Rx-to-OTC switch criteria/policy in the United States, United Kingdom, Canada, Japan and Australia. A new approach to investigating US FDA's overall switch regulatory policies through the combined application of casehistory evaluations, electronic survey questionnaire and telephone interviews has been utilized. This investigation was conducted in three phases. Phase-1 involved information retrieval and a critical review of existing literature, phase-2 applied switch case history analyses pertinent to US FDA and phase-3 measured the attitudes/opinions of the academic/professional community and key opinion leaders in nonprescription medicines across the US, Canada, UK and Australia on important questions. The subject matter of this dissertation is of enormous current interest in the global nonprescription medicines arena. The significance of the results presented in this dissertation is amplified as this area has received little academic attention and this is perhaps the first comprehensive treatment of this subject. Overall, inferences based on the information elicited have been summarized to provide data-based responses to questions of global interest in the self-medication arena. This information is especially valuable to the US FDA as they are currently seeking public comment. Data shows that the OTC regulatory model in the United States may be improved. Evidence indicates that principles upon which approaches for improvement of the US regulatory system must be based should include: an objective evaluation of pharmacist class of OTC medicines, development of effective consumer education tools, increase in regulation of non-traditional OTC medicines, acknowledge that not all disease conditions and drug classes are suitable for self-treatment, a collaborative approach by FDA towards switching that includes all stakeholders is more favored, decisions on switch petitions must be case-specific without a presumptive bias and public health benefit must be the paramount evaluation criterion.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".