Evidence map›Paper›PMID 29264063›Full record

ArticleJournal of general and family medicine2017

Cluster analysis of pharmacists' work attitudes.

Keiichi Nakagomi, Yukikazu Hayashi, Takako Komiyama

Abstract read
In one paragraph

Article in Journal of general and family medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Cluster analysis of pharmacists' work attitudes.Journal of general and family medicine · 2017
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Keiichi NakagomiFaculty of Pharmacy Musashino University Tokyo Japan.
Yukikazu HayashiDevelopment Strategy Division A2 Healthcare Corporation Tokyo Japan.
Takako KomiyamaSchool of Pharmacy Kitasato University Tokyo Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Few studies in Japan use clustering to examine the work attitudes of pharmacists. This study conducts an exploratory analysis to classify those attitudes based on previous studies to help staff pharmacists and their management to understand their mutually beneficial requirements. Methods: Survey data collected in previous studies from 1 228 community pharmacists and 419 hospital pharmacists were analyzed using Quantification Theory 3 and clustering. Results: Among community pharmacists, two clusters, namely 30- to 34-year-old married males and married males aged over 35 years, reported the highest job satisfaction, intending to remain in their jobs for 5 years or more or until retirement. Conversely, one cluster of 35- to 39-year-old single females reported the lowest job satisfaction and intended to remain for less than 5  years or were undecided. Among hospital pharmacists, one cluster of 22- to 25-year-old single males reported the highest job satisfaction and intended to remain for more than 5 years. Conversely, one cluster of 30- to 34-year-old married males reported the lowest job satisfaction and a period of working undetermined. Conclusions: This study used clustering to explore how pharmacists of different ages, marital statuses, and experience felt regarding their work. Job satisfaction and human relationships are significant in considering future work plans of practicing pharmacists. Pharmacy staff, supervisors, and managers of community or hospital pharmacies must recognize features of pharmacists' work attitudes for offering high-quality service to patients.

Indexed as

cluster analysiscommunity pharmacisthospital pharmacistjob satisfactionpharmacist surveywork attitude

Identifiers

PMID29264063
PMCPMC5729359

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.