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Record W2495585134 · doi:10.1177/1715163516652423

Pharmacists’ reactions to natural disasters

2016· article· en· W2495585134 on OpenAlexvenueaboutno aff
Denise A. Epp, Yoshirou Tanno, Anita Brown, Bob S. Brown

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2016
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsNatural disasterWork (physics)Natural (archaeology)Medical careMedicineMedical emergencyNursingHistoryGeographyEngineering

Abstract

fetched live from OpenAlex

Pharmacists engage in patient-centred care on a daily basis, often without realizing the importance of their role. In this article, we tell the story of pharmacists’ responses to patients during recent natural disasters in Japan and Canada, to show that beyond everyday work commitments, pharmacists can go a step further in patient care to ensure the safety of patients and to meet their medical needs in devastating situations. The disasters that are described here were very different, but these pharmacists gave their time and energy to make sure their patients’ needs were met throughout the disaster period and after. At the time these interviews were conducted (summer 2015), years after the disasters, the pharmacists responded that they were taking steps to make sure that they could be more effective in their future patient care, as well.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.062
GPT teacher head0.368
Teacher spread0.306 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations10
Published2016
Admission routes2
Has abstractyes

Explore more

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