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Record W3090564239 · doi:10.1177/0840470420961522

Challenges and success strategies for dyad leadership model in healthcare

2020· article· en· W3090564239 on OpenAlex
Anurag Saxena

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.

Bibliographic record

VenueHealthcare Management Forum · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDyadContext (archaeology)Health careInterpersonal communicationPsychologyKnowledge managementPerceptionInterpersonal relationshipSocial psychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The use of a dyad leadership model involving a physician co-leader and a co-leader with a different background, the dyad co-leader, is gradually increasing in Healthcare Organizations (HCOs). There is a paucity of empirical studies on various aspects of this model. This study's aim was to identify challenges and strategies for success in the dyad leadership model in healthcare. Through a mixed-methods approach utilizing focus groups, surveys, and semi-structured interviews, perceptions of 37 leaders in one HCO at different hierarchical levels were analysed based on their lived experiences. The challenges and success strategies spanned personal, interpersonal, and organizational domains. The areas requiring attention included mindsets, competencies, interpersonal relationship, support, time, communication, and collaboration. In addition, the importance of organizational context addressing its structure, strategy, operations, and culture was highlighted. The findings from this study may be used for praxis, development, and implementation of dyad leadership.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.499
GPT teacher head0.481
Teacher spread0.018 · 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