An early evaluation of team consistency and scope optimization in team-based cancer care
Bibliographic record
Abstract
BACKGROUND: The British Columbia (BC) government has made significant investments towards the implementation of team-based care (TBC) in its provincial comprehensive cancer control program. TBC implementation involves purposeful efforts towards: (a) establishing/expanding multidisciplinary care teams, (b) optimizing scope of practice, and (c) increasing care team consistency. Study objectives include an early-phase evaluation of (i) the association between TBC elements and team effectiveness and (ii) staff perceptions of barrier and facilitators of team effectiveness. METHODS: A series of five surveys over a 2-year period will be administered to prospectively evaluate the ongoing implementation of TBC. This study draws on data from the first of the five planned surveys, administered in May 2023. Eligible respondents included 299 program employees-spanning various roles such as physicians, nurses, and unit clerks-working within TBC at the time of survey deployment. The survey included both validated and researcher-developed questions that were either closed or open-ended, including measures of team composition, team consistency, team effectiveness, scope of practice, and demographics. Quantitative data were analyzed using descriptive and regression analysis; qualitative data were analyzed guided by interpretive description methodology. RESULTS: Collected responses totaled 121, with the majority of respondents being women (76%), full-time employees (90%), and working in direct patient care (77%). Regression analyses indicated that (i) higher frequency of consistently working with the same team members and (ii) lower proportion of shifts practicing below scope are both significant predictors of higher team effectiveness ratings. Qualitative data highlighted staffing levels as a driver of under- and over-utilized scopes of practice. Furthermore, effective communication, enhanced knowledge of each team member's scope of practice, and strong interpersonal relationships were highlighted as contributing factors to effectiveness among multidisciplinary care teams. CONCLUSIONS: Preliminary findings from the first of five prospective surveys highlight team consistency and role optimization as drivers of effective teamwork in the early implementation of a team-based model of cancer care. Future research should explore contextual factors that influence cancer care staff and clinicians' perceptions of effectiveness.
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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 | 0.071 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".