MétaCan
Menu
Back to cohort
Record W3172722312

An Exploration of the Utility of Appreciative Inquiry for Job Crafting and Wellbeing Promotion

2021· dissertation· en· W3172722312 on OpenAlex
Ekaterina Pogrebtsova

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Atrium (University of Guelph) · 2021
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicAppreciative Inquiry and Organizational Change
Canadian institutionsnot available
Fundersnot available
KeywordsAppreciative inquiryPromotion (chess)PsychologyPedagogyPublic relationsPolitical scienceEngineering ethicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

A thriving society is made possible when educators experience holistic and sustained wellbeing: feeling engaged, purposeful, happy, and effective at work, and in turn, providing an enriching learning environment for students. It is therefore a societal concern that teaching professionals report high levels of employee stress, burnout, and disengagement. The aim of the current dissertation is to not only understand how to alleviate the problems in the education profession, but to improve educators’ sense of wellbeing and work engagement. I conducted two studies exploring how the theory and practice of Appreciative Inquiry (AI) can be applied in brief and longer-term interventions to capitalize on “what’s working well” in education currently, and how educators and institutions can create a more optimal future of education. Secondly, I integrated job crafting theory to advance understanding of how AI can be applied to empower educators to create positive changes in their work, lives, and institutions to promote personal and organizational benefits. This dissertation is presented in manuscript format with an opening chapter summarizing the current state of the AI and job crafting literature. The first manuscript (Chapter 2) is a qualitative study with a sample of 17 Kindergarten to Grade 12 (K-12) teachers in Canada and the US showcasing how a foundational method of AI—the AI interview—can help educators understand how to promote their wellbeing and more personally desirable work experiences. Manuscript 2 (Chapter 3) is a qualitative case study following the experiences of 22 faculty members in a Canadian University as they participated in a 6-month program grounded in AI and job crafting theories. Finally, in Manuscript 3 (Chapter 4), I propose a guiding framework on how the AI process can facilitate job crafting. I also propose eight practical recommendations for planning and implementing an AI-facilitated job crafting intervention to promote employee wellbeing as well as larger-scale positive organizational change initiatives. This dissertation concludes with a summary chapter of all three manuscripts. Together, this dissertation progresses the nascent exploration of how researchers and practitioners alike can promote employee wellbeing and positive organizational change using an integrated AI and job crafting approach.

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)0.000
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 score0.726
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.261
Teacher spread0.201 · 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