Breast cancer care in Alberta: a Patients perspective
Bibliographic record
Abstract
IntroductionEach year in Alberta, over 2,300 women are affected by breast cancer. In Alberta, a multi-year Breast Health Initiative is underway to improve breast cancer care; reduce wait times, coordinate care, and enhance patient experience. Patient reported experience measurements are important to inform and advance patient and family-centred care.
 Objectives and ApproachThe aim is to assess breast cancer patients’ experiences at two survey points; after surgeon consult and after breast surgery. Patients meeting inclusion criteria; highly suspicious of cancer on imaging result (i.e. BI-RADS 5), referral to Calgary or Edmonton breast program, English speaking, and having an email address are recruited by RN coordinators or nurse navigators. Automated survey invitations from REDCap are used. Seven days after the surgeon consult the first survey is sent and seven days after breast surgery the second survey is sent.
 ResultsPatient recruitment began November 27, 2017 and January 2, 2018 for Edmonton and Calgary, respectively. As of February, 2018, 45 patients had been recruited. Of these, the first survey was sent to 34 (i.e. seven days post surgeon consult) and 19 (56%) had completed the survey. All those eligible (18) agreed to participate in the upcoming second survey. Of those, six had provided their surgery date and the second survey which both were completed. Recruitment is ongoing until the conference, at that time there will be sufficient numbers to report findings.
 Conclusion/ImplicationsPatient and family-centred care is an element of high-quality healthcare which AHS has identified as a priority. These results will report on the breast cancer patients’ perspectives and generate important information for clinicians and administrators to use for decision making and quality improvement of health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".