MétaCan
Menu
Back to cohort
Record W1569493185 · doi:10.5430/jha.v4n3p93

Guide to strategic planning in critical care medicine

2015· article· en· W1569493185 on OpenAlexaffvenue
Kwadwo Kyeremanteng, Gianni D’Egidio

Bibliographic record

VenueJournal of Hospital Administration · 2015
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsStrategic planningBurnoutHealth careBusinessStrategic human resource planningNursingPopulationMedicinePublic relationsMarketingPolitical science

Abstract

fetched live from OpenAlex

Strategic planning is increasing in value. More organizations are implementing such strategies to help achieve their corporate goals. Strategic planning can help improve morale and satisfaction amongst staff, managers and stakeholders. It can help improve efficiency within the organization and ideally the quality of care as well. Most importantly, it helps an organization to focus and prioritize its goals therefore increasing its chances for success. Critical care medicine is a unique branch of medicine because of the high costs associated with care. It is reasonable to perceive that these costs will go up in the future. Our population is aging and our abilities to sustain life are constantly improving. ICU is also unique because it is associated with high stress for the patients, families, nurses, physicians and allied health professionals. Care is often associated with post-traumatic stress and burnout. For these reasons strategic planning is essential for critical care to thrive, provide good care for patients and to allow for efficient use of resources. There are several approaches to strategic planning. This article will look at the 10-step method described in Bryson’s Strategic Planning for Public and Non-Profit Organizations and how it can apply to critical care medicine.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.153
GPT teacher head0.520
Teacher spread0.367 · 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 designQualitative
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

Citations2
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Hospital AdministrationSame topicDisaster Response and ManagementFrench-language works237,207