A comparative analysis of mission statement content in secular and faith‐based hospitals
Bibliographic record
Abstract
Purpose This paper seeks to determine whether significant differences exist between secular and faith‐based hospitals in terms of specific mission statement components and mission‐related performance variables. Design/methodology/approach A total of 130 top managers from a sample of 515 Canadian hospitals responded to a comprehensive questionnaire investigating 23 mission statement components and seven mission performance outcome measures. Data were analyzed using frequency analysis, one‐way analysis of variance, MANOVA, chi‐squared and Mann‐Whitney U tests. Findings The analysis showed that differences in mission content exist between different types of hospitals, and that these differences form a pattern of sorts within each type. It was also found that faith‐based hospitals out‐perform their secular counterparts in many ways. Research limitations/implications The research and its findings are limited in their application to relatively large Canadian health care organizations and the responses/opinions given by managers from a hospital's senior echelons. Practical implications The results have implications for all health care organizations interested in improving the results in their mission performance scorecard. The findings both confirm the impact that mission statements can have on selected hospital performance indicators and demonstrate that faith‐based hospitals have been more diligent in taking advantage of them. Originality/value This is the first paper to show that specific and significant differences exist between the mission statements of secular and faith‐based hospitals and that those differences are associated with hospital performance. These findings will be of special interest to senior hospital administrators and “directors of mission” within faith‐based institutions.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".