Cancer Center Staff Satisfaction: Descriptive Results of a Canadian Study
Bibliographic record
Abstract
Caring for cancer patients is generally considered very rewarding work, but it can also be stressful and demanding. Therefore, it is important for oncology healthcare professionals to feel satisfied with their work environment in order to provide the best care possible. An ethics-approved 61-item staff satisfaction survey was developed in-house to gain insights regarding workplace satisfaction among all staff at The Ottawa Hospital Cancer Center. Descriptive statistics were used to analyze the responses. A total of 478 individuals completed the online survey, with 75.1% women, 23.2% men, and 1.7% preferring not to say. This represented the vast majority (>75%) of cancer center staff. The approximate breakdown according to healthcare professional type was as follows: 21% nurses, 20% radiation therapists, 18% physicians, 13% clerical staff, and 28% other types of staff. Almost all (97.4%) generally enjoyed their work, with 60% stating "very much" and 37.4% stating "a little bit", and 93.3% found working with cancer patients rewarding. The overall satisfaction level at work was high, with 30.1% reporting "very satisfied" and 54.2% "somewhat satisfied". However, in terms of their work being stressful, 18.6% stated it was "very much" and 62.1% "a little bit". Also, in terms of their workload, 61.3% stated it was "very busy" and 10% stated it was "excessively busy". The most enjoyable aspects of work were listed as interactions with colleagues, interactions with patients, and learning new things. The least enjoyable aspects of work were excessive workload, a perceived unsupportive work environment, and technology problems. Levels of satisfaction and stress at work varied according to role at the cancer center. Most cancer center staff seem to enjoy their work and find it rewarding. However, the work environment can be challenging and stressful. Areas for improvement include managing workloads, ensuring staff feel supported, and improving the user-friendliness of technology.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".