Regulatory uncertainties in the pharmaceutical sector: Perceptions among Nigerian pharmacists and policy implications for decision making
Bibliographic record
Abstract
Regulatory uncertainty (RU) is a component of general environmental uncertainties. Perceived regulatory uncertainties by Nigerian Pharmacists and policy implications were assessed using a questionnaire of 13-item inventory, self-administered to pharmacists in a captive situation during the Annual National Conference of the Pharmaceutical Society of Nigeria in November 2014. Participants were given the context of RU to avoid ambiguity and improve the quality of response. Six items addressed pharmacists’ knowledge of impact of regulatory uncertainties in pharmacy practice; three addressed social regulation and policy issues in the pharmaceutical sector, three dealt with perceived policy implications, while one item was on demographic characteristics. Descriptive statistics was used to analyze the results. A total of 507 pharmacists comprising: 42 (8.28%), 316 (62.33%), 37 (7.30%), 86 (16.96%) and 26 (5.13%) pharmacists in the industry, community, government (technocrats), hospital and academia respectively, completed the questionnaires. Vast majority (98%, 92% respectively) indicated that inadequate legislative control would increase business risk and that RU within professional body could cause varied enforcement decisions. A total of 95% were aware of the involvement of other stakeholders in pharmacy practice. Majority (73%) indicated correctly that an increase in RU would cause a decrease in predictability of law enforcement as well as a negative effect on the stimulation of investment in the pharmaceutical sector. Also a high majority recognized the role of politics on the outcomes in pharmaceutical sector. A high percentage (92.5%) recognized that collaborative approach to policy making, and implementation would reduce acrimony and cost of social regulation, and increase the benefits of essential products. Some responses did not reflect in-depth understanding of the influence of RU on investment decisions and policy implications in Nigeria. These prompt the need to educate pharmacists on regulatory issues, the adaptation of policy options, market dynamics, and their stake in the complex pharmaceutical sector to enable them achieve greater control on the management of uncertainty. Structures, systems and strategies should be matched with political and financial resources, and policy guidelines to achieve laudable health outcomes. Models of relationships for achieving best regulatory practices are presented.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".