Inclusion and Exclusion – How Staff Experience Belonging at a Mental Health and Addiction Hospital Setting: A Cross-Sectional Study and the Implications
Bibliographic record
Abstract
The need for belonging is a fundamental human motivation. Despite the significance of belonging, many people struggle to feel a sense of it. Healthcare organizations continue to experience workforce shortages. A workplace that does not promote belonging may prevent the career progression of its staff, leading to low morale and poor work performance. This may negatively impact their physical and mental health and compromise patient safety. The purpose was to explore inter-professional healthcare workers’ sense of belonging at all levels (horizontal and vertical) and to predict possible factors that may promote/hinder it. An anonymous, descriptive, cross-sectional online electronic survey design and a modified version of the Sense of Belonging Instrument were used to collect data over 2 months in 2024 to report employees’ levels of belonging. This included mental health nurses. The study was conducted at a large, urban, mental health and addiction hospital located in Ontario, Canada. A total of 441 staff members completed a questionnaire. The response rate was 24%. The variables of age, tenure, gender, ethnicity, area of work, and job satisfaction were statistically significant. Multiple regression analysis revealed that the variables of tenure, gender, employment status, and job satisfaction were predictors for belonging. Healthcare leaders must understand who feels that they belong and who does not. Every employee (regardless of their background), should feel that they belong. People should not have to feel like an outsider when they are at work. Understanding and fostering a sense of belonging in the workplace is critical to maintaining a stable workforce.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".