APPLIED HEALTH SERVICES RESEARCH AS A FRAMEWORK FOR PATIENT-ORIENTED RESEARCH: A SUGGESTED FRAMEWORK FOR HEALTH CARE RESEARCHERS
Bibliographic record
Abstract
Engaging the general population in the research process provides new visions that may lead to innovations and research that are relevant to patients. Many developed countries like Canada are working toward engaging the population in healthcare research to achieve outcomes pertaining to enhanced accountability, transparency, and population empowerment in research. For example, Canada created Canada's Strategy for Patient-Oriented Research (SPOR) (Canadian Institute of Health Research [CIHR], 2011) to empower the patient's role in health research and the healthcare system. However, there appears to be a gap in the literature because few studies or reports could be found on how applied health services research might be used as a framework for patient-oriented research. The aims of these authors in this paper are to (1) discuss how the applied health services research (AHSR) can be used as a framework for patient-oriented research (POR); and (2) describe salient challenges and potential outcomes that may result from implementing applied health research as a framework for patient-oriented research. This is a multidimensional framework for patient engagement using AHSR as a framework for POR as they have shared crossover research aspects between them. Conducting POR at different levels of AHSR reduces the gap between health research and practice, and empower patients to be responsible for their own health and health services (Gooberman-Hill et al., 2013). The multidisciplinary nature of AHSR and POR may face challenges related to research interests, patients, patient involvement, environmental/ organizational regulations and policies, and research culture.
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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 | 0.431 | 0.182 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.016 | 0.147 |
| Scholarly communication | 0.037 | 0.042 |
| Open science | 0.014 | 0.029 |
| Research integrity | 0.028 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".