Towards the use of mixed methods inquiry as best practice in health outcomes 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
Mixed methods research (MMR) has found an increased interest in the field of health outcomes research. Consideration for both qualitative and quantitative perspectives has become key to contextualising patient experiences in a clinically meaningful measurement framework. The purpose of this paper is to outline a process for incorporating MMR in health outcomes research to guide stakeholders in their understanding of the essence of mixed methods inquiry. In addition, this paper will outline the benefits and challenges of MMR and describe the types of support needed for designing and conducting robust MMR measurement studies. MMR involves the application of a well-defined and pre-specified research design that articulates purposely and prospectively, qualitative and quantitative components to generate an integrated set of evidence addressing a single research question. Various methodological design options are possible depending on the research question. MMR designs allow a research question to be studied thoroughly from different perspectives. When applied, it allows the strengths of one approach to complement the restrictions of another. Among other applications, MMR can be used to enhance the creation of conceptual models and development of new instruments, to interpret the meaningfulness of outcomes in a clinical study from the patient perspective, and inform health care policy. Robust MMR requires research teams with experience in both qualitative and quantitative research. Moreover, a thorough understanding of the underlying principles of MMR is recommended at the point of study conception all the way through to implementation and knowledge dissemination. The framework outlined in this paper is designed to encourage health outcomes researchers to apply MMR to their research and to facilitate innovative, patient-centred methodological solutions to address the complex challenges of the field.
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 | 0.038 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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