What is your research question? An introduction to the PICOT format for clinicians.
Bibliographic record
Abstract
Clinicians often witness impressive treatment results in practice and may wish to pursue research to formally explore their anecdotal experiences. The potential to further new knowledge both within the profession and to the greater healthcare system is compelling. An obvious next step for a practitioner considering research is to connect with experienced researchers to convey their idea for a study, who may in turn ask, “What is your research question?” With limited understanding of how to respond, this interaction may result in the first and last experience these clinicians will have with the research community. It has been estimated that between 1% and 7% of the chiropractic profession in Canada is engaged in research. 1,2 Arguably, this low engagement could be the result of practitioners’ perceived importance of research and levels of research literacy and capacity. However, increasing demands for evidence-based approaches across the health system puts pressure on all clinicians to base their decisions on the best available scientific evidence. Lack of clinician representation in research has the probable effect of limiting growth and new developments for the profession. Furthermore, lack of clinician involvement in research complicates the transfer of study findings into practical settings. The Canadian Institutes of Health Research describes integrated knowledge translation as a process that involves collaboration between researchers and knowledge users at all stages of a research project.3 This necessitates involvement of clinicians to help in forming a research question, interpreting the results, and moving research findings into practice. This shared effort between clinicians and researchers increases the likelihood that research initiatives will be relevant to practice.3 Conversely, it has been reported that there is a growing communication gap between clinicians and academics in chiropractic. 4 Clinicians have important practice-related questions to ask, but many may lack the ability to map out their research strategy, specifically in communicating their question in a manner required to develop a research protocol. David L. Sackett, Officer of the Order of Canada and the founding Chair of Canada’s first Department of Clinical Epidemiology & Biostatistics at McMaster University, highlights the importance of mapping one’s research strategy in exploration of the research question: “one-third of a trial’s time between the germ of your idea and its publication in the New England Journal of Medicine should be spent fighting about the research question.” (personal communication, November 30, 2011) We describe a randomized controlled trial (RCT) example to highlight how clinicians may use existing literature and the PICOT format to formulate a research question on treatment efficacy.
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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 | 0.031 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.171 | 0.104 |
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".