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Record W2151776278 · doi:10.1177/1527154022374

Nursing Human Resource Planning in Alberta: What Went Wrong?

2002· article· en· W2151776278 on OpenAlex
Shannon D. Scott, Carole A. Estabrooks, Daniel Cohn, Carolee Pollock

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePolicy Politics & Nursing Practice · 2002
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsSimon Fraser UniversityUniversity of Alberta
Fundersnot available
KeywordsEconomic shortageNursing shortageNursingPoliticsPopulationHealth careHuman resourcesMedicineResource (disambiguation)Political scienceNurse educationEconomic growthGovernment (linguistics)Environmental healthEconomics

Abstract

fetched live from OpenAlex

Health care organizations in most of the Western world are struggling with a shortage of nurses. An aging population with greater health care needs, a graying profession, declining nursing school enrollments, and more career choices for women all contribute to the shortage. The extent of the Canadian registered nursing shortage is predicted to reach between 60,000 and 112,000 by 2011. Through this policy analysis, it is shown how a number of factors conspired to create the current nursing shortage in Alberta. Through the analysis of qualitative interviews with key stakeholders, five themes are identified as factors causing this shortage. These themes are national and provincial political contexts during the 1990s, increased need for nurses, lack of timely information, nurses’ political inexperience, and a loss of institutional nursing leadership. Recommendations to address the crisis are also presented.

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 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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.109
GPT teacher head0.522
Teacher spread0.413 · 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