Pharmacist-led medication-related needs assessment in rural Ghana
Bibliographic record
Abstract
Access to both essential and non-essential medications is increasing worldwide. While increased drug access is a positive development, many countries lack the infrastructure for appropriate distribution, administration, and monitoring of drug therapy. The objective of this study was to assess medication and pharmacy-related needs in the rural Ashanti Region of Ghana and to determine barriers of achieving optimal health outcomes in this region. Qualitative domains and associated themes were identified by observations from integration into community culture and from conduction of semi-structured interviews with local community leaders, health workers, or those with knowledge of health-related issues. Eight semi-structured interviews were completed and four thematic domains were identified; access to care, resource shortages, medication safety, and education/training. Barriers and challenges identified under each thematic domain included (but were not limited to) availability of clean water sources, shortages of medications and diagnostic equipment, financial considerations, misunderstanding of medication indications and directions for use, and shortages of qualified pharmacy or dispensary staff. Most respondents also expressed a need for continuing education and training of healthcare personnel. It can be concluded that there is a need for development of health services related to medications. Locally supported interventions and future research should focus on barriers and challenges identified from the thematic domains.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".