Participant Directed Mobile Interviews: A Data Collection Method for Conducting In-Situ Field Research at a Distance
Bibliographic record
Abstract
COVID-19 restrictions necessitated innovative online adaptations to conventional qualitative methods; however, virtual interviews do not permit capturing visual data from participants’ environments. Traditional mobile interviews conducted in situ provide contextual, relational, and situational knowledge. Virtual adaptations of mobile interviews have been theorized but not fully tested. This paper compares experiences with an online interview and a virtual adaptation of a mobile interview, the Participant-Directed Mobile Interview (PDMI), during a pilot study examining the design elements of private dental office waiting rooms as symbolic presentations of a dentist’s and dental clinic’s identity. Participants ( n = 4), who worked in private dental clinics and had participated in the planning and designing the waiting room, were selected using a purposive and convenience sample design. Participants were required to have access to a mobile device, the internet, and the Zoom cloud-based video conferencing platform. A semi-structured interview preceded PDMI, and both were recorded on Zoom. Unlike the online semi-structured interview, PDMI revealed the participant’s relationship to the space, produced more nuanced and contextual data and clarified the meaning of subjective statements and terms. Mobile devices used by the participant (iPad/mobile phone) provided the researcher with a view of the space and access to visual and relational data that would not be possible if the camera focused on the participants alone. Participants could freely explore, interact with, and reflect on the space in real-time, enhancing the depth and breadth of responses. PDMI was limited by participants’ access to and choice of equipment and internet services and their technical skill level. This technique could be applied to circumstances beyond the COVID-19 pandemic. PDMI could increase access, reduce research costs in distant or remote communities, and provide valuable insights within various methodologies and disciplines.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".