Using care pathways for cancer diagnosis in primary care: a qualitative study to understand family physicians’ mental models
Bibliographic record
Abstract
BACKGROUND: Care pathways are tools that can help family physicians navigate the complexities of the cancer diagnostic process. Our objective was to examine the mental models associated with using care pathways for cancer diagnosis of a group of family physicians in Alberta. METHODS: We conducted a qualitative study using cognitive task analysis, with interviews in the primary care setting between February and March 2021. Family physicians whose practices were not heavily oriented toward patients with cancer and who did not work closely with specialized cancer clinics were recruited with the support of the Alberta Medical Association and leveraging our familiarity with Alberta's Primary Care Networks. We conducted simulation exercise interviews with 3 pathway examples over Zoom, and we analyzed data using both macrocognition theory and thematic analysis. RESULTS: Eight family physicians participated. Macrocognitive functions (and subthemes) related to mental models were sense-making and learning (confirmation and validation, guidance and support, and sense-giving to patients), care coordination and diagnostic decision-making (shared understanding). Themes related to the use of the pathways were limited use in diagnosis decisions, use in guiding and supporting referral, only relevant and easy-to-process information, and easily accessible. INTERPRETATION: Our findings suggest the importance of designing pathways intentionally for streamlined integration into family physicians' practices, highlighting the need for co-design approaches. Pathways were identified as a tool that, used in combination with other tools, may help gather information and support cancer diagnosis decisions, with the goals of improving patient outcomes and care experience.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".