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Record W4412036050 · doi:10.1111/jmft.70050

Deepening Understanding of Family Dynamics in Chinese Culture: Reflective Writing in Professional Education

2025· article· en· W4412036050 on OpenAlexaff
Pei‐Chin Lin, Katie Chang, Ping‐Chuan Hsiung

Bibliographic record

VenueJournal of Marital and Family Therapy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsReading (process)PsychologyIndividuationTheme (computing)Dynamics (music)PedagogyDevelopmental psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Understanding one's family of origin is essential for students in the helping professions, as it fosters self-awareness and illuminates personal strengths and vulnerabilities. This qualitative study examines the family experiences of Taiwanese undergraduate social work students, focusing on cultural influences on self-development and the effectiveness of reading and reflective writing in deepening family exploration. Content analysis of 34 student assignments identified two main themes: (1) key issues emerging during family exploration, and (2) the role of reading and reflective writing in facilitating this process. Subthemes of the first theme included individuation and differentiation, the need for parental affirmation, parent-child conflict, and the influence of parents' marital relationships. Findings highlight the enduring impact of Chinese cultural values on students' development and family dynamics. Engaging with culturally relevant case literature and reflective writing promoted deeper insights and self-awareness, underscoring the importance of integrating family exploration into culturally responsive professional education.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.420
Teacher spread0.382 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractyes

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