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Record W2070261496 · doi:10.12927/hcq.2013.23187

An Extra-organizational Mentorship Pilot for Canadian Health Leaders

2012· article· en· W2070261496 on OpenAlexaboutno aff
Paul Castonguay

Bibliographic record

VenueHealthcare Quarterly · 2012
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
Fundersnot available
KeywordsMentorshipPrivate sectorPublic sectorPublic relationsHealth careBusinessBest practiceProcess (computing)Health sectorPublic healthNursingPolitical scienceMedicineEconomic growthMedical educationHealth servicesManagementEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

There are two sectors in the Canadian health ecosystem: the public sector, composed of hospitals, and the private sector, consisting of suppliers of drugs and services; both are aimed at providing optimal patient care. Currently, both sectors are struggling with the uncertainty and unpredictability plaguing the health environment. A mentoring pilot was aimed at providing solutions for both sectors by strengthening leadership development and accelerating the relationships with organizations from the other sector. The extra-organizational mentoring program included people from Roche Canada (private sector) and hospitals (public sector) whose participants are members of the Canadian College of Health Leaders. An evaluation of the program demonstrated that it was a positive and productive leadership development process for the majority of participants. The mentoring pilot helped advance partnerships based on trust and respect across the two sectors. The pragmatic process and demonstrable success of the program have gained far-reaching attention, and the program has influenced the development of other mentorship initiatives. Extra-organizational mentoring should be encouraged and actively developed with other health organizations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.102
GPT teacher head0.382
Teacher spread0.280 · 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 designNot applicable
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

Citations1
Published2012
Admission routes1
Has abstractyes

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