Living documents: A longitudinal data collection method for health services research
Bibliographic record
Abstract
Qualitative research tools offer health sciences researchers the ability to understand complex, varied, and nuanced facets of an individual's lived experiences. Several of these tools include observations, interviews, and focus groups, each with its own advantages and limitations. We created an alternative tool, the Living Document, an iterative, longitudinal, open-ended, and adaptable questionnaire that overcomes the barriers presented by other qualitative research tools. The Living Documents allows researchers to better understand and familiarize themselves with the research context, understand change over time, and capture the perspectives of research participants. As a proof of concept, the Living Document was employed within a chronic disease program embedded within primary care called the Best Care COPD (BCC) program to better understand its growth and implementation in new patient sites. Given the iterative and sequential nature of the tool employed within the BCC program, its compatibility with other data collection tools, and its longitudinal use, the Living Document was shown to be a valuable tool for the field of health sciences and for implementation research.
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.
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 | 0.098 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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, 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".