Psychographic profiling — a method for developing relatable avatars for a direct-to-consumer mobile game for adolescent girls on mobile in India
Bibliographic record
Abstract
Abstract This research aimed to conduct psychographic profiling for developing a direct-to-consumer mobile game targeting the sexual and reproductive health of adolescent girls in India. We used semi-structured tools to collect information on role models, family, education, dreams, fears and decision-making power. We also presented visual stimuli to the participants (Indian girls; age: 15–19 years; N = 103). Responding to the stimuli, the participants expressed their perceptions of social norms, moral standards, obligations and aspirations. We carried out thematic analysis using predetermined codes and did an inductive analysis to identify emergent profiles. Analysis revealed seven primary themes that influence the participants’ aspirations, decisions and agency: (i) clarity regarding career goals, (ii) information about the pathways to reach those goals, (iii) efforts toward achieving goals, (iv) clarity about priorities, (v) parental support, (vi) ability and willingness to negotiate and (vii) social mobility. Based on combinations of these themes, four predominant personas emerged as descriptive categories of girls’ lives and attitudes. These four profiles will form the basis of Game of Choice, Not Chance™ game: informing the scenarios, in-game decisions and relatable content. This study represents a novel approach to research for an equally innovative game for agency building and health awareness among adolescents.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".