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Record W4388802100 · doi:10.1016/j.rcsop.2023.100374

Self-care in New Zealand: The role of the community pharmacy

2023· article· en· W4388802100 on OpenAlexaff
Yasmin Abdul Aziz, Susan Heydon

Bibliographic record

VenueExploratory Research in Clinical and Social Pharmacy · 2023
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPharmacyCLARITYHealth carePublic relationsCommunity pharmacyBusinessCommunity healthNursingMedicineEconomic growthPolitical sciencePublic health

Abstract

fetched live from OpenAlex

Self-care is a growing area in community pharmacy globally. In a time where people are taking control over their own health care, the question of the degree of self-care available from community pharmacies is pertinent. New Zealand is a country that has publicly funded healthcare; with over 1000 community pharmacies catering to a population of 5 million people. Despite the availability and accessibility of community pharmacies, much remains unknown about how self-care is offered in community pharmacies and the extent to which it is provided. In addition to this lack of clarity, is the current period of reorganisation occurring in the New Zealand healthcare system. The current changes involve dis-establishing district health boards (DHBs) and unifying the New Zealand health system into one streamlined system. It leads us to question whether this move will change funding allocations and health priorities as well as how they affect service provision in community pharmacy. While research has shown that New Zealand is paving the way in medicines reclassification making medicines more accessible, other research shows a fragmentation exists in services provided by community pharmacies in the area of self-care. This article will highlight what is currently known about self-care in New Zealand, the gaps that exist and the current challenges in this area.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
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 head0.626
GPT teacher head0.604
Teacher spread0.022 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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