Cross-sector, sessional employment of pharmacists in rural hospitals in Australia and New Zealand: a qualitative study exploring pharmacists’ perceptions and experiences

Background: Many rural hospitals in Australia and New Zealand do not have an on-site pharmacist. Sessional employment of a local pharmacist offers a potential solution to address the clinical service needs of non-pharmacist rural hospitals. This study explored sessional service models involving pharmacists and factors (enablers and challenges) impacting on these models, with a view to informing future sessional employment. Methods: A series of semi-structured one-on-one interviews was conducted with rural pharmacists with experience, or intention to practise, in a sessional employment role in Australia and New Zealand. Participants were identified via relevant newsletters, discussion forums and referrals from contacts. Interviews were conducted during August 2012-January 2013 via telephone or Skype™, for approximately 40–55 minutes each, and recorded.Results: Seventeen pharmacists were interviewed: eight with ongoing sessional roles, five with sessional experience, and four working towards sessional employment. Most participants provided sessional hospital services on a weekly basis, mainly focusing on inpatient medication review and consultation. Recognition of the value of pharmacists’ involvement and engagement with other healthcare providers facilitated establishment and continuity of sessional services. Funds pooled from various sources supplemented some pharmacists’ remuneration in the absence of designated government funding. Enhanced employment opportunities, district support and flexibility in services facilitated the continuous operation of the sessional service. Conclusions: There is potential to address clinical pharmacy service needs in rural hospitals by cross-sector employment of pharmacists. The reported sessional model arrangements, factors impacting on sessional employment of pharmacists and learnings shared by the participants should assist development of similar models in other rural communities

The role of basic science in evidence-based medicine

Proponents of Evidence-based medicine (EBM) do not provide a clear role for basic science in therapeutic decision making. Of what they do say about basic science, most of it is negative. Basic science resides on the lower tiers of EBM's hierarchy of evidence. Therapeutic decisions, according to proponents of EBM, should be informed by evidence from randomised studies (and systematic reviews of randomised studies) rather than basic science. A framework of models explicates the links between the mechanisms of basic science, experimental inquiry, and observed data. Relying on the framework of models I show that basic science often plays a role not only in specifying experiments, but also analysing and interpreting the data that is provided. Further, and contradicting what is implied in EBM's hierarchy of evidence, appeals to basic science are often required to apply clinical research to therapeutic questions. © 2010 Springer Science+Business Media B.V

Safer dispensing labels for prescription medicines

The standard way in which directions are represented on dispensing labels can be misinterpreted. Errors in interpreting instructions are more common in people with low health literacy and when the timing of administration is not specified. Improving written communication on prescriptions and dispensing labels can reduce medication\ua0errors. There is an emerging international consensus on best-practice communication on dispensing labels

A problem for achieving informed choice

Most agree that, if all else is equal, patients should be provided with enough information about proposed medical therapies to allow them to make an informed decision about what, if anything, they wish to receive. This is the principle of informed choice; it is closely related to the notion of informed consent. Contemporary clinical trials are analysed according to classical statistics. This paper puts forward the argument that classical statistics does not provide the right sort of information for informing choice. The notion of probability used by classical statistics is complex and difficult to communicate. Therapeutic decisions are best informed by statistical approaches that assign probabilities to hypotheses about the benefits and harms of therapies. Bayesian approaches to statistical inference provide such probabilities