An essential medicines list in Ireland: A qualitative interview study of interest-holders
Bibliographic record
Abstract
Abstract Background The World Health Organization recommends each country develop a national essential medicines list (NEML), prioritising a core medicines set aligned with national health needs. Policy in Ireland partly focusses on prescribing costs and quality, which creates opportunities for NEML development. We aimed to explore interest-holders’ perspectives on an Irish NEML. Methods We applied a descriptive qualitative methodology. Using purposive and snowball sampling, we recruited interest-holders from Irish bodies/groups with roles in shaping Ireland’s medicines policy/use with potential for involvement in a NEML. Semi-structured interviews were conducted and analysis involved Braun and Clarke’s six-stage approach to thematic analysis. Results Thirteen participants were interviewed and three themes were generated: 1) the NEML’s purpose, 2) NEML barriers and facilitators and 3) development and implementation processes. For participants, an NEML’s purpose is meeting the population’s priority needs. Participants also outlined roles in ensuring adequate supplies and as a national formulary. Views differed on whether an NEML should involve reduced costs to patients for access. Participants proposed that the national government health department, the state body who run the health service (HSE), and/or the medicines regulator (HPRA) should be responsible for an NEML. Conclusions Participants perceived an NEML in Ireland as beneficial and aligning with the WHO’s vision: medicines that effectively and safely treat the priority healthcare needs of the population. Future work should explore the patients’ and the public’s perspectives on an NEML. Other countries’ NEMLs offer exemplars to inform Ireland’s approach. Research in Context What is already known about the topic? According to The World Health Organization, "each country has the direct responsibility of evaluating and adopting a list of essential drugs, according to its own [health] policy." These national essential medicines lists (NEMLs) can be used to standardising medications used across healthcare institutions. This can have benefits for procurement costs, patient safety and preventing shortages. What does this study add to the literature? We interviewed 13 medicines policy interest-holders about an Irish NEML. Participants envisioned an NEML as meeting the population’s priority needs. They also outlined roles for an NEML in ensuring supplies and as a national formulary. Barriers (e.g. achieving consensus) and facilitators (e.g. buy-in) to developing and implementing an NEML were outlined. Participants said transparent development processes, the use of financial incentives and a communications strategy, could help overcome barriers. Participants proposed that the national government health department, the state body who run the health service (HSE), and/or the medicines regulator (HPRA) should be responsible for an NEML. What are the policy implications? An NEML could fit into Ireland’s ongoing health reform policy: Sláintecare. Participants’ suggested strategies for overcome NEML barriers are backed-up by findings in other high-income countries (e.g. Sweden and Canada) where transparency, financial incentives and a communications strategy contributed to NEML success.
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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".