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Record W4213204064 · doi:10.1093/geront/gnw162.295

A DIVERSITY TRAINING EVALUATION FRAMEWORK FOR THE COMMUNITY HEALTH AND AGED CARE SECTOR

2016· article· en· W4213204064 on OpenAlexaboutno aff

Bibliographic record

VenueThe Gerontologist · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)Training (meteorology)Diversity trainingHealth sectorHealth careNursingGerontologyPsychologyMedicineGeographyPolitical scienceEconomic growthEnvironmental healthHealth servicesEconomics

Abstract

fetched live from OpenAlex

program consisted of multiple sessions over a 2-day period for staff as well as an open public lecture for family, friends and others who were interested in the program. We collected survey data from 44 LTC staff (e.g., personal support workers, licensed nurses, recreation staff), 25 in Ontario and 19 in Saskatchewan; and 44 family members and others (n= 21 from Ontario, 23 from Saskatchewan). The majority of participants rated the training program as excellent, stating "it's just basic human care". All participants stated that they now understand the purpose of Namaste Care. Most participants stated that they learned how to interact with residents in the Namaste room and the types of programming that are offered. Similarly, participants who attended the public lecture stated that they were very satisfied with the education, stating that the public lecture helped them learn more about how the program can be implemented. Participants in both groups suggested having follow-up sessions with a 'report back' about how the program impacts resident outcomes. These study findings support the use of a facility-wide educational program to help launch a new innovation in LTC.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0150.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.365
GPT teacher head0.506
Teacher spread0.141 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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