Predicting Professional Psychological Help-Seeking Intentions for Indians Through Envisioning Counseling and Psychotherapy as Western Cultural Healing Practices
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".