Evidence map›Paper›PMID 37415949›Full record

ArticleHeliyon2023

Spatial-temporal evolution pattern and optimization path of family education policy: An LDA thematic model approach.

Chenwei Ma, Chen Qirui

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

2 authors.

Chenwei MaSchool of Public Administration, Sichuan University, Chengdu, 610065, China.
Chen QiruiSchool of Public Administration, Sichuan University, Chengdu, 610065, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Family education policy plays a crucial role in modernizing family education. By examining the temporal and spatial evolution of this policy, its inherent logic, constructs, and optimal pathways can be better understood. The study analyzed local family education policy documents, extracting six major themes using the Latent Dirichlet Allocation (LDA) model, and presented them according to the calculated mean theme probability. The themes include parental ability, school security, institutional environment, government support, social coordination, and high-quality development. Parental ability and government support were found to be particularly prominent, suggesting that many local policies focus on enhancing parents' skills for delivering family education and bolstering the government's role in public affairs. This combines the dual responsibilities of being an educational entity and accountable subject in the joint development of family education. Understanding the characteristics and variations in temporal and spatial distribution can enrich family education policy design, fostering the high-quality development of family education initiatives. Based on the findings, the study proposes three optimization paths for policy design: promotion and empowerment (building a multi-cooperative system), regional interconnection (understanding the current state of local policies and leveraging their strengths), and breaking barriers (simultaneously promoting the inclusiveness of family education and brand development). This study emphasizes the needs of customizing family education policy based on the temporal and spatial features and local requirements for maximum outputs.

Indexed as

Family education policyLDA topic modelSpatial featuresTemporal features

Identifiers

PMID37415949
PMCPMC10320304

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.