Do hospital balanced scorecard measures reflect cause-effect relationships?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate whether first level measures in the Balanced Scorecard (BSC) declaring a cause-effect relationship by design are composite indices of lower measures, and if they converge into a single factor as is traditionally accepted in the BSC literature. Design/methodology/approach This study reports results of a quantitative case study that focusses on an Ontario (Canada) community hospital that has been using the BSC. Findings The results of this study challenge the cause-effect assumption of the BSC, particularly in a cascading context, and suggest that a lack of attention of how composite indices of lower measures converge into a single higher level measure may be the reason for ineffective use of the BSC. Research limitations/implications The BSC is a dynamic tool; as such there are several measures that have a very short history, thus limiting the observations available to be used in statistical models. Practical implications A key recommendation for practice that emerges from this study is the need to test if lower level metrics do merge naturally in the upper level measure of the BSC; if not, the upper level measure might not be linked to other measures rendering the BSC ineffective in the context of causality. Originality/value Although several studies have argued in favour of the cause-effect relationship of the BSC, none of those found in the literature have paid attention to the way in which first level measures are constructed. This may explain why certain measures are linked, while others are not, to those that are calculated as composite indices of several lower level indicators.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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 it