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Translating knowledge to benefit the patient: Are you ready? *

2008· article· en· W2144821991 on OpenAlexvenueaboutno aff
Peter Thomson

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2008
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineComputer sciencePsychology

Abstract

fetched live from OpenAlex

OVER THE PAST 10 YEARS I HAVE GIVEN A NUMBER OF TALKS TO pharmacists about gastroesophageal reflux disease (GERD). During that time, I have wondered how well pharmacists have been able to take that knowledge and apply it to making a difference in the care of their patients. I am very much a neophyte when it comes to theoretical and formal education on the topic of knowledge translation. Rather, I am more of an old goat who teaches by experience and knowledge of the literature. My friends who know this area much better have provided me with some references on the science of knowledge translation to guide me. As defined by the Canadian Institutes of Health Research, knowledge translation is “the exchange synthesis and ethically sound application of knowledge — within a complex system of interactions among researchers and users....”1 Those of us who provide education, formally or informally, try to transfer knowledge in such a way that those receiving the information can implement it in their practice. Knowledge translation implies that there is a personal relationship between the information provider and the person targeted to apply that knowledge. This relationship is based on trust. Knowledge translation is more than just continuing education — it focuses on health outcomes and changing behaviours. So why have I chosen to digress down the path of knowledge translation for these guidelines on GERD? Well, my biggest fear is that all the information in this supplement may not be put into practice by the pharmacists who receive it. Ask yourself, do you know patients on chronic acid suppression therapy who sound like good candidates for intermittent or on-demand therapy?2 Everyone dispensing these agents should be able to think of a number of patients who sound like good candidates. Are you going to call the prescribers or talk to the patients to get them to try different therapy? You may run the risk of unhappy physicians or upset patients if you suggest this. My sense of the ambulatory care setting is that physicians may consider on-demand therapy an acceptable option but very few are advising patients to pursue it. It is an excellent area for pharmacists to take a proactive role in optimizing GERD therapy. In order to do that, most pharmacists will need to feel comfortable with the benefits and risks associated with making an active intervention. As knowledge providers, we hope that this supplement and the additional resources we cite will give you sufficient information regarding these benefits and risks, as well as inspire you enough that you will recommend these changes. Besides on-demand therapy, in what other key areas of GERD management can pharmacists take a more proactive role in patient care? First, pharmacists should ensure that patients with signs of serious gastrointestinal pathology obtain timely medical care. How? By talking to the patient and, on occasion, the prescriber. There are many other areas of GERD management (some controversial) that you might also want to ponder, such as: • When can patients on a twice-a-day proton pump inhibitor (PPI) be stepped down to a once-a-day PPI? • Should pharmacists recommend that patients with long-standing GERD talk to their physician about getting an endoscopy to look for Barrett’s esophagus? • Should pharmacists recommend that patients talk to a physician about acid suppressive therapy for asthma, chronic cough or other potential respiratory manifestations of GERD? • What advice should pharmacists give to patients asking about the risk of pneumonia or osteoporosis with PPI use? More than anything, I hope this supplement will inspire you to make an effort to acquire additional knowledge in key areas of GERD management. Hopefully, it will lead to more inspired and confident pharmacists contributing to patient care. ■

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.074
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0090.015
Open science0.0010.005
Research integrity0.0090.016
Insufficient payload (model declined to judge)0.0370.026

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.

Opus teacher head0.050
GPT teacher head0.282
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2008
Admission routes2
Has abstractyes

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