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Record W4391923554 · doi:10.1093/ajhp/zxae044

The need for an emergency planning and preparedness strategic plan for pharmacy leadership

2024· article· en· W4391923554 on OpenAlexaff
Kaitlyn E. Watson, Jason Chou, Deborah Simonson

Bibliographic record

VenueAmerican Journal of Health-System Pharmacy · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDisk formattingPreparednessPlan (archaeology)PharmacyStrategic planningPublic relationsOperations researchLibrary scienceComputer scienceEngineeringPolitical scienceBusinessHistoryMarketingLaw

Abstract

fetched live from OpenAlex

There is nothing new about disasters and emergencies impacting our society. The 2022 ASHP statement on the role of the pharmacy workforce in emergency preparedness stated, “Healthcare systems must continue to engage their employees, leaders, and communities to prepare for these unforeseen events to alleviate extended disruption of services for their patients in a time of need.”1 But what does this mean for pharmacy leadership? The path to a consistent, proactive process in minimizing the impact of emergency situations begins with pharmacy leadership teams. We need to build strong pharmacy leadership teams that are prepared to respond and not simply react to the situation presented in front of them. From this strong foundation, we can lead our pharmacy workforce through the planning and responding to a health emergency and beyond. Additionally, it is not up to others to dictate the pharmacy emergency planning and preparedness strategic plan. However, others will step in and tell us how pharmacy should respond in the absence of us having detailed plans and routinely undertaking health emergency planning and preparedness activities.2 Below are some steps to consider when building the road map for an emergency planning and preparedness strategic plan.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.424
GPT teacher head0.533
Teacher spread0.108 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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