Bibliographic record
Abstract
<div> Medicines are essential commodities that form the cornerstone in majority of processes and interventions aimed at assuring optimal healthcare and wellbeing for any population. Apart from being saddled with the responsibility of providing medications for this purpose, the pharmaceutical industry has the potential to catalyse socioeconomic development such as job creation and revenue generation. This study aimed at assessing government’s role in driving development in Nigeria’s pharmaceutical sector. Questionnaires were administered to healthcare practitioners that participated in an event targeted at developing Nigerian pharmaceutical sector. Data collected were analysed using Statistical Package for Social Sciences. A total of 76 respondents participated in the study. Two-thirds of the study participants (69.7%) were males, slightly above a third of the study participants (38.2%) were aged 51 and above, and close to a quarter of the participants (21.1%) were doctorate degree holders. About half of the study participants (51.4%) indicated that Nigerian pharmaceutical sector was not adequately regulated, whilst almost all (97.4%) indicated that engaging the legislature was critical for the development of the sector. A strong majority of the study participants (87.5%) indicated that existing drug laws should be reviewed so as to protect the pharmaceutical sector. Also, majority of the participants (56.3%) were not satisfied with government’s efforts in developing the pharmaceutical industry. Although this study explored a small cohort, its findings have revealed novel insights regarding factors limiting the requisite prioritisation of the Nigerian pharmaceutical sector. The emergent evidence can begin to underpin proactive policy and practice reforms aimed at achieving medicines’ security in Nigeria. Further studies can build on these preliminary findings to enable robust and comprehensive sectoral interventions that improve access to healthcare, whilst also catalysing socioeconomic development. </div>
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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) | 0.190 | 0.012 |
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; both teacher heads agree on what is shown here.
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".