Alternation as a form of allocation for quality improvement studies in primary healthcare settings: the on-off study design
Bibliographic record
Abstract
BACKGROUND: Randomized controlled trials are considered the "gold standard" for scientific rigor in the assessment of benefits and harms of interventions in healthcare. They may not always be feasible, however, when evaluating quality improvement interventions in real-world healthcare settings. Non-randomized controlled trials (NCTs) are designed to answer questions of effectiveness of interventions in routine clinical practice to inform a decision or process. The on-off NCT design is a relatively new design where participant allocation is by alternation. In alternation, eligible patients are allocated to the intervention "on" or control "off " groups in time series dependent sequential clusters. METHODS: We used two quality improvement studies undertaken in a Canadian primary care setting to illustrate the features of the on-off design. We also explored the perceptions and experiences of healthcare providers tasked with implementing the on-off study design. RESULTS AND DISCUSSION: The on-off design successfully allocated patients to intervention and control groups. Imbalances between baseline variables were attributed to chance, with no detectable biases. However, healthcare providers' perspectives and experiences with the design in practice reveal some conflict. Specifically, providers described the process of allocating patients to the off group as unethical and immoral, feeling it was in direct conflict with their professional principle of providing care for all. The degree of dissatisfaction seemed exacerbated by: 1) the patient population involved (e.g., patient population viewed as high-risk (e.g., depressed or suicidal)), 2) conducting assessments without taking action (e.g., administering the PHQ-9 and not acting on the results), and 3) the (non-blinded) allocation process. CONCLUSIONS: Alternation, as in the on-off design, is a credible form of allocation. The conflict reported by healthcare providers in implementing the design, while not unique to the on-off design, may be alleviated by greater emphasis on the purpose of the research and having research assistants allocate patients and collect data instead of the healthcare providers implementing the trial. In addition, consultation with front-line staff implementing the trials with an on-off design on appropriateness to the setting (e.g., alignment with professional values and the patient population served) may be beneficial. TRIAL REGISTRATION: Health Eating and Active Living with Diabetes: ClinicalTrials.gov identifier: NCT00991380. Date registered: 7 October 2009. Controlled trial of a collaborative primary care team model for patients with diabetes and depression: Clintrials.gov Identifier: NCT01328639 Date registered: 30 March 2011.
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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 | 0.339 | 0.418 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".