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Record W7162001549 · doi:10.82308/48652

The effect of appreciative inquiry on the retention of nurses and other professionals and on the development of innovative ideas in health care /

2007· dissertation· en· W7162001549 on OpenAlex
Marie-Claire. Richer

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

Venuenot available
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicAppreciative Inquiry and Organizational Change
Canadian institutionsnot available
Fundersnot available
KeywordsAppreciative inquiryHealth careJob satisfactionPsychological interventionQualitative researchQuality (philosophy)Qualitative propertyPopulation health

Abstract

fetched live from OpenAlex

Background. Factors such as the shortage of personnel, hospital closures and mergers, the ageing of the population combined with the evolution of medical technologies have all added pressure to the health care system. These pressures have had an impact on current work environments and health care workers' satisfaction. In light of the evidence on the influence of job satisfaction and retention on the quality of tare and patient safety, interventions are needed to address these issues. Objective. The objective were to examine the effect of Appreciative Inquiry on: (1) the development of innovative ideas regarding work organization; (2) changes in health care professionals' and particularly nurses' job satisfaction and intent to stay; and, (3) levels of empowerment, social network and perceived organizational support. Research design. The study used a multiple embedded case study design to access the multifaceted aspects relating to retention and innovation and examine the changes engendered by an AI intervention. Participants and setting. Two oncology ambulatory clinics constituted the cases; the embedded units were the health care and the management teams (N = 47 and 5) of an adult oncology division in a multi-site university affiliated health care centre in a large Canadian city. Methods. Multiple sources of evidence were used. They consisted of participant observation, questionnaires providing quantitative and qualitative evidence, interviews, direct observation and documentation. Results. AI provided a way to involve health care professionals in change processes by creating the opportunity and the conditions that promoted the emergent of innovative ideas. Some of these ideas were implemented during the study period. AI did not result in improved psychological empowerment, perceived organizational support, support network or decreased intent to stay. Job satisfaction decreased over time. Perceptions of organizational support and psychological empowerment were found to influence job satisfaction and intent to stay. Conclusion. This study makes a contribution to micro-systems examination of change processes and reveals how ideas evolve and are developed in a multidisciplinary context. AI represents a way to tap into the innovative potential of individuals within an organization. The findings suggest that upper and middle management support is required throughout change processes and that multilevel interventions need to be pursued to facilitate the implementation of innovative ideas and subsequently improve work environments.

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)0.001
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 score0.649
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.341
Teacher spread0.303 · 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