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Record W7037800536

Evaluation of a collective reflective coaching device to sustain early childhood education managers well-being during covid-19

2022· other· en· W7037800536 on OpenAlexaboutno aff

Bibliographic record

VenueArchipelago (University of Quebec in Montreal) · 2022
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicColeoptera Taxonomy and Distribution
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingStaffingEarly childhood educationReflexivityWork (physics)Professional developmentReflective practiceQuality (philosophy)Early childhood
DOInot available

Abstract

fetched live from OpenAlex

Background. 
\nWork well-being of early childhood education and care (ECEC) managers is essential to provide educational quality services to children (Corr et al., 2017). If some factors are known to influence work well-being, such as job stress, burnout, depressive symptoms, self-compassion, and work engagement (Cumming & Wong, 2019; Rothmann, 2008; Zessin, 2015), COVID-19 pandemic appears to be deleterious (Bigras et al., 2021), particularly because of the frequent adjustments induced by public health measures and staffing shortages. In a previous study, ECEC managers expressed a need for support to face the challenges encountered during the pandemic (Bigras et al., 2021). To meet their need and support their work well-being, a professional coaching process, through a collective reflexive group, had been implemented (Bigras et al., 2021). Coaching modalities involving reflective practice were included to ensure its effectiveness, considering its value for dealing with complex problems, as ECEC managers did since the pandemic (Susman-Stillman et al., 2020). 
\n
\nObjective. 
\nThis study aims to evaluate the effects of a collective reflective coaching device intended for ECEC managers on the factors linked with work well-being (self-compassion, work-related stress, burnout, depressive symptoms, and work engagement) during the pandemic. Methods. This research uses a quasi-experimental design (pre-post) with a control group. Experimental group involves 39 ECEC managers from some regional areas in Quebec (Canada) and was recruited with the help of the regional grouping of ECEC of Monteregie. The experimental group, divided in four subgroups, met for three hours every four weeks between February and June 2021. The meetings focused on topics based on managers' needs to support their work well-
\nbeing (e.g., stress, self-compassion, self-care). Control group involves 43 ECEC managers from the same regional areas recruited by email. Quantitative data were collected with an online questionnaire (LimeSurvey) completed before the first meeting and after the last one for both experimental and
\ncontrol groups. Social desirability was measured for the two groups, at both pre and post times. 
\n
\nResults.
\nANCOVA analyses controlling for pre-test scores were conducted. Since the control group (M = 0.71, SD= 0.19) had higher pre-test score on social desirability than the experimental group (M = 0.61; SD =0.16), t(79.115)=-2.561, p=0.012, we controlled for this variable. Preliminary results indicated that each variable is explained by the pre-test scores (p>0.001). Also, the descriptive data at pre-test indicated that participants in experimental group had lower pre-test scores and that they tended to reach the means score of control group on post-test scores for each variable. 
\n
\nConclusion. 
\nPreliminary results suggest that reflective support system could be beneficial to improve well-being of ECEC managers through COVID-19 because theirs scores improved between the beginning and the end of the meetings. It is possible that participants in the experimental group joined the program because they needed support for their well-being. Nevertheless, since managers must perform their job with high quality in ECEC, it seems imperative to ensure that they receive all the support and resources they need to mitigate negative influences of the pandemic on their well-being.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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.

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
Published2022
Admission routes1
Has abstractyes

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