Evidence map›Paper›PMID 33445714›Full record

ArticleInternational journal of environmental research and public health2021

Degrees of Shortage and Uncovered Ratios for Long-Term Care in Taiwan's Regions: Evidence from Dynamic DEA.

Kuo-Feng Wu, Jin-Li Hu, Hawjeng Chiou

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  8. Interventions to Improve Physical Capability of Older Adults with Mild Disabilities: A Case Study.International journal of environmental research and public health · 2022
    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.

Kuo-Feng WuDepartment of Nurse-Midwifery and Women Health, National Taipei University of Nursing and Health Science, Taipei City 112303, Taiwan.ORCID 0000-0003-4037-9476
Jin-Li HuInstitute of Business and Management, National Yang Ming Chiao Tung University, Taipei City 10044, Taiwan.ORCID 0000-0002-7337-2935
Hawjeng ChiouCollege of Management, National Taiwan Normal University, No. 162, Section 1, Heping E. Rd., Taipei City 10610, Taiwan.ORCID 0000-0002-4408-8345

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The government is facing the country's aging population and low birth rate have led to a severe shortage of its healthcare workforce in Taiwan after 2003. In order to explore the status of the country's degree of long-term care shortage and uncovered ratio, this research uses the Push-Pull-Mooring (PPM) theory to explain long-term care efficiency during 2010-2019 in each city and county. We collect longitudinal-sectional data for 2010-2019 from the Ministry of Health and Welfare's Department of Statistics for 22 administrative regions in Taiwan in each year and employ dynamic data envelopment analysis (DEA) to evaluate the overall technical efficiency and the disaggregate output insufficiency to explain the research results. The main findings are as follows: (1) Cities near the capital Taipei have the highest degree of shortages in long-term caregivers and high uncovered ratios of people who need long-term care. (2) Presently, there is no demand to increase the number of long-term care institutions in Taiwan. (3) The government should introduce new long-term care certificates through national examinations in order to develop a stronger professional workforce in this field.

Indexed as

Birth RateLong-Term CareAgedEfficiencyHumansTaiwandata envelopment analysisdegree of shortagelong-term careuncovered ratio

Identifiers

PMID33445714
PMCPMC7828166

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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.