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Record W2508860550 · doi:10.1080/19371918.2016.1188749

The Study of Living Conditions and Perceived Needs for Social Security among Clergy in China: A Case Study of Guanzhong Qinling Area

2016· article· en· W2508860550 on OpenAlexaff
Shaoguo Zhai, Qi Zhuang, Pei Wang, Zhaoxi Wang, Peter C. Coyte

Bibliographic record

VenueSocial Work in Public Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRespondentChinaSocial securityMeditationSocial supportSocial needsPsychologyEconomic securityNursingSocioeconomicsEconomic growthMedicineSociologyPolitical scienceSocial psychologyHealth careLawGeography

Abstract

fetched live from OpenAlex

This study used quantitative and qualitative data collected in the Guanzhong Qinling area of China to examine living conditions and perceived needs for social security among men and women of the clergy. The survey finds that most respondent clergy are Buddhists. When the clergy have economic difficulties, their main types of support include self-support (28.8%), help from other believers (25.6%), and assistance from other community residents (18.4%). When the clergy are old, they tend to live alone (25%), receive institutional care from religious organizations (19%), and receive support from other believers (18%). When the clergy are ill, they will often select self-treatment (primarily the use of traditional Chinese medicine [25%], and spiritual healing [25%], including meditation, prayers, and psychotherapy) and receive treatment at hospitals (20%). The study found that the clergy perceived their needs for social security as either great (19.7%) or modest (36.5%). Very few clergy (10%) indicate the absence of social security needs. Most clergy believe that the key social security priorities should be medical care (34%), elderly care (29%), and charitable assistance (21%).

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.080
GPT teacher head0.405
Teacher spread0.325 · 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 designQualitative
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

Citations2
Published2016
Admission routes1
Has abstractyes

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