Using Timeline Methodology to Facilitate Qualitative Interviews to Explore Sexuality Experiences of Female Pakistani-Descent Immigrant Adolescents
Bibliographic record
Abstract
In qualitative research, there is a growing interest in understanding the use of timelines in combination with other qualitative methods. In this paper, we will address how the creation of timelines facilitated and informed the process of semi-structured interviews. We used an interpretive descriptive qualitative study to understand the perceptions and experiences of developing sexuality among female adolescents of Pakistani descent, and timelines were used as a part of the semi-structured interview process. Timelines were created in a participatory way in which girls were asked to recount significant events related to their sexuality. We found that the methodological combinations within qualitative research such as semi-structured interviews and timelines have the potential to advance knowledge regarding the experience of immigrant female adolescents’ sexuality. Using the timeline strategy to collect data helped in building rapport with the participants, allowed the participants to become active partners and navigate the process, and helped them to think about future resolutions through reflection.
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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 | 0.040 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".