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Record W4404361833 · doi:10.1027/2157-3891/a000109

Predicting Professional Psychological Help-Seeking Intentions for Indians Through Envisioning Counseling and Psychotherapy as Western Cultural Healing Practices

2024· article· en· W4404361833 on OpenAlexaff
Robinder P. Bedi, Jessica Y. Ahn, Rajneesh Choubisa, Namita Ruparel

Bibliographic record

VenueInternational Perspectives in Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychotherapistPsychologyClinical psychology

Abstract

fetched live from OpenAlex

Abstract: There remains considerable ambiguity in predicting which Indians seek professional psychological services during times of distress and which do not. This study expands past research on predicting professional psychological help-seeking attitudes of Indians to help-seeking intentions. Drawing on variables previously examined as predictors of help-seeking attitudes from a frame of psychotherapy as a manifestation of Western culture, this study aimed to investigate the predictive ability of six cultural variables (Asian values, European American values, importance of one’s ethnic group to their identity, commitment to one’s ethnic group, westernization, and cultural mistrust). Participants were 377 university students from India. The results can be taken to suggest that a highly westernized lifestyle and greater adherence to European American values are best predictive of professional psychological help-seeking intentions among Indians. Assessing these two variables will enable practitioners to direct prospective clients to culturally congruent treatment methods that they are most likely to attend and perhaps benefit more from. Overall, the findings of this study are in line with conceptualizing professional psychological treatment as a manifestation of Western culture.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.070
GPT teacher head0.472
Teacher spread0.402 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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