Developing and Implementing a Triangulation Protocol for Qualitative Health Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, the authors present an empirical example of triangulation in qualitative health research. The Canadian Heart Health Dissemination Project (CHHDP) involves a national examination of capacity building and dissemination undertaken within a series of provincial dissemination projects. The Project's focus is on the context, processes, and impacts of health promotion capacity building and dissemination. The authors collected qualitative data within a parallel-case study design using key informant interviews as well as document analysis. Given the range of qualitative data sets used, it is essential to triangulate the data to address completeness, convergence, and dissonance of key themes. Although one finds no shortage of admonitions in the literature that it must be done, there is little guidance with respect to operationalizing a triangulation process. Consequently, the authors are feeling their way through the process, using this opportunity to develop, implement, and reflect on a triangulation protocol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.199 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it