The Life Story Board as a Tool for Qualitative Research
Bibliographic record
Abstract
Within the context of a study about the lived experiences of Indigenous males living with HIV in Vancouver, Canada, we explored the utilization of an innovative method of collecting the narratives of study participants. This article describes and assesses the use of the Life Story Board (LSB) as a potentially rich interview tool for qualitative research and explores the process, as well as its advantages and challenges. The LSB uses sets of cards, markers, and notation on a play board to create a visual representation of a verbal narration about someone’s life situation or story. Five study participants took part in a conventional face-to-face interview and 4 months later were interviewed with the use of the LSB. These study participants were asked toward the end of the LSB session about their experience of being interviewed with and without the LSB. Data were also gathered from the interviewers’ experience. The findings suggested that the LSB offers interesting opportunities when used in qualitative research. Study participants found it to facilitate a reflective and more in-depth narration of their lived experience. The interviewer’s perspective for the most part corroborated these observations.
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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 | 0.106 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".