Patient and Physician Perceptions of Lung Cancer Care in a Multidisciplinary Clinic Model
Bibliographic record
Abstract
Background: Lung cancer (lc) is a complex disease requiring coordination of multiple health care professionals. A recently implemented lc multidisciplinary clinic (mdc) at Kingston Health Sciences Centre, an academic tertiary care hospital, improved timeliness of oncology assessment and treatment. This study describes patient, caregiver, and physician experiences in the mdc. Methods: We qualitatively studied patient, caregiver, and physician experiences in a traditional siloed care model and in the mdc model. We used purposive sampling to conduct semi-structured interviews with patients and caregivers who received care in one of the models and with physicians who worked in both models. Thematic design by open coding in the ATLAS.ti software application (ATLAS.ti Scientific Software Development, Berlin, Germany) was used to analyze the data. Results: Participation by 6 of 72 identified patients from the traditional model and 6 of 40 identified patients from the mdc model was obtained. Of 9 physicians who provided care in both models, 8 were interviewed (2 respirologists, 2 medical oncologists, 4 radiation oncologists). Four themes emerged: communication and collaboration, efficiency, quality of care, and effect on patient outcomes. Patients in both models had positive impressions of their care. Patients in the mdc frequently reported convenience and a positive effect of family presence at appointments. Physicians reported that the mdc improved communication and collegiality, clinic efficiency, patient outcomes and satisfaction, and consistency of information provided to patients. Physicians identified lack of clinic space as an area for mdc improvement. Conclusions: This qualitative study found that a lc mdc facilitated patient communication and physician collaboration, improved quality of care, and had a perceived positive effect on patient outcomes.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".