Developing an Initial Program Theory to Explain How Patient-Reported Outcomes Are Used in Health Care Settings: Methodological Process and Lessons Learned
Bibliographic record
Abstract
A central aspect of any theory-driven realist investigation (synthesis or evaluation) is to develop an initial program theory (IPT). An IPT can be used to frame and understand how, for whom, why, and under what contexts complex interventions work or not. Despite well-established evidence that IPTs are a central aspect to any realist investigation, there is wide variation and a lack of methodological discussion on how to develop an IPT. In this article, we present the approach that we used to develop an IPT of how patient-reported outcomes (PROs) are used in health care settings. Specifically, we completed a systematic review to extract tacit theories reported in the literature. The benefit of this approach was that it provided a rigorous review of the literature in the development of IPTs. The challenges included (1) rediscovering what is already well established in the theoretical literature, (2) generating an overabundance of partial candidate theories, and (3) extensive use of time and resources for what was the first stage to our larger funded research study. Our recommendations to other scholars considering this approach are to ensure that they (1) live within their means and (2) narrow the scope of the research question and/or develop a conceptual framework using middle-range theories. These methodological insights are highly relevant to researchers embarking on a realist investigation, tasked with developing an IPT.
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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 | 0.396 | 0.405 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".