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Record W4403910863 · doi:10.12927/hcq.2024.27426

Emerging Through Adversity: Early Implementation Learnings of Leadership Capacity Supports to Bolster Team Resilience

2024· article· en· W4403910863 on OpenAlexaffvenueabout
Rosanra Yoon, Nilusha Jiwani-Ebrahim, Julia Roitenberg

Bibliographic record

VenueHealthcare Quarterly · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsRegional Municipality of NiagaraUniversity of Toronto
Fundersnot available
KeywordsBolsterResilience (materials science)PsychologyBest practicePsychological resiliencePublic relationsPolitical scienceNursingMedicineSocial psychologyEngineeringLaw

Abstract

fetched live from OpenAlex

The COVID-19 pandemic challenged the health workforce to respond to rapidly changing circumstances that demanded agility and endurance. As we emerged from those early years of the pandemic, we saw the aftermath of fatigue and waning resilience. The support of leaders is fundamental to the success of teams, especially in times of adversity and recovery. This perspective paper shares the early learnings from a rapid implementation of leadership supports to bolster team resilience across a local public health agency in Ontario. The Team Resilience Initiative aimed to strengthen participatory leadership capacities, psychological safety and team cohesion.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.296
Teacher spread0.252 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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