Knowledge, attitudes, and practices regarding contraception amongst community pharmacy staff: a cross-sectional study in Nigeria
Bibliographic record
Abstract
Introduction Lack of access to effective contraception methods can lead to an increased prevalence of unintended pregnancies, as well as possible deleterious health consequences. Community pharmacies represent the most accessible gateway for population medicines' and healthcare needs. Regarding contraceptives, they have also been identified as a platform for the provision of additional services, such as complementary counselling. This study aimed at assessing the knowledge, attitudes, and practices of community pharmacy staff towards contraception. Methods A cross-sectional study was undertaken in the Federal Capital Territory, Nigeria. Data were collected from 315 community pharmacy staff using self-administered questionnaires. The participants' knowledge and attitude scores were categorised using Bloom's cut-off point. Analyses were undertaken using Statistical Package for Social Sciences. The data were analysed using frequency distribution, chi-square, and linear regression at a 5% level of significance. Results Male participants in the study (165/315, 52.4%) were slightly higher than the female respondents (150/315, 47.6%), and about two-thirds of the study cohort were pharmacists (200/315, 63.5%). The majority of the participants (183/315, 58.1%) had poor knowledge of contraceptive use. A quarter of them (81/315, 25.7%) reported moderate attitudes. Almost all the participants (279/298, 93.6%) indicated recommending contraceptives for married adults, and a significant proportion of them (136/292, 45.5%) were opposed to recommending contraceptives for unmarried adolescents. Study respondents' professional role was also identified as a significant influence on their knowledge of contraceptives and contraception ( p < 0.001). Conclusion Findings from this study revealed poor knowledge and negative attitudes of community pharmacy staff towards contraception. Government and relevant stakeholders can build on these novel findings to reform pertinent contextual policies and practices. This can significantly improve access to contraceptives amongst the populace, and consequently reduce unintended pregnancies alongside possible health and societal implications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".