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Record W2081574542 · doi:10.1258/jhsrp.2007.007013

Implementing a Balanced Scorecard as a Strategic Management Tool in a Long-Term Care Organization

2008· article· en· W2081574542 on OpenAlexaffabout
Corinne Schalm

Bibliographic record

VenueJournal of Health Services Research & Policy · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsShepherds Care Foundation
Fundersnot available
KeywordsBalanced scorecardProcess managementStrategic planningPerformance indicatorStrategy mapBusinessPerformance measurementStrategic managementKnowledge managementComputer scienceMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: The Capital Care Group, the largest public sector continuing care organization in Canada, had no ready access to information on its own performance and therefore was limited in its pursuit of evidence-informed decision-making. To remedy this, it was decided to introduce a balanced scorecard. ASSESSMENT OF PROBLEM: A literature review was conducted together with interviews with 10 other health care organizations which had implemented balanced scorecards. With this information, a workshop was held that resulted in a framework and about 120 potential indicators. Subsequently the number of indicators was reduced to 29, using pre-determined criteria. RESULTS: Development of a corporate balanced scorecard facilitated executive strategic thinking and clarified the organization's strategic direction. In parallel, scorecards were developed at the level of care centres. These had a common core of indicators, plus some site-specific ones. Development of the corporate scorecard took three years and an additional six months for the care centre scorecards. STRATEGIES FOR CHANGE: A formal implementation plan has been accepted by the executive team. Key to this is communicating to staff the role of scorecards for strategic management and not just performance measurement. Traditional thinking needs to change from a short-term operational focus to long-term strategy. In addition, champions need to be identified in each care centre and they need to be networked together. Finally, the scorecard is being integrated into existing operational management as a routine component together with resources to support its use. LESSONS AND MESSAGES: The balanced scorecard has focused on its role as a strategic management tool. The indicators and dimensions need to be customized to the organization. Senior management must be seen to be driving its introduction. It is worth spending sufficient time developing and implementing a scorecard rather than trying to rush its introduction. The scorecard needs to be integrated with existing management processes and sufficient resources must be assigned. However, success will ultimately depend on the culture of the organization being appropriate and receptive.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.365
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2008
Admission routes2
Has abstractyes

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