Patient, family and professional suggestions for pandemic-related surgical backlog recovery: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Surgical shutdowns related to the COVID-19 pandemic have resulted in prolonged wait times for nonemergency surgery. We aimed to understand informational needs and generate suggestions on management of the surgical backlog in the context of the ongoing COVID-19 pandemic through focus groups with key stakeholders. METHODS: We performed a qualitative study with focus groups held between Sept. 29 and Nov. 30, 2021, in Ontario, with patients who underwent or were awaiting surgery during the pandemic and their family members, and health care leaders with experience or influence overseeing the delivery of surgical services. We conducted the focus groups virtually; focus groups for patients and family members were conducted separately from health care leaders to ensure participants could speak freely about their experiences. Our goal was to elicit information on the impact of communication about the surgical backlog, how this communication may be improved, and to generate and prioritize suggestions to address the backlog. Data were mapped onto 2 complementary frameworks that categorized approaches to reduction in wait times and strategies to improve health care delivery. RESULTS: A total of 11 patients and family members and 20 health care leaders (7 nursing surgical directors, 10 surgeons and 3 administrators) participated in 7 focus groups (2 patient and family, and 5 health care leader). Participants reported receiving conflicting information about the surgical backlog. Suggestions for communication about the backlog included unified messaging from a single source with clear language to educate the public. Participants prioritized the following suggestions for surgical recovery: increase supply through focusing on system efficiencies and maintaining or increasing health care personnel; incorporate patient-centred outcomes into triage definitions; and refine strategies for performance management to understand and measure inequities between surgeons and centres, and consider the impact of funding incentives on "nonpriority" procedures. INTERPRETATION: Patients and their families and health care leaders experienced a lack of communication about the surgical backlog and suggested this information should come from a single source; key suggestions to manage the surgical backlog included a focus on system efficiencies, incorporation of patient-centred outcomes into triage definitions, and improving the measurement of wait times to monitor health system performance. The suggestions generated in this study that may be used to address surgical backlog recovery in the Canadian setting.
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 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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".