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Record W3170710475 · doi:10.1108/sgpe-06-2020-0035

Insights from a survey “comments” section: extending research on doctoral well-being

2021· article· en· W3170710475 on OpenAlex

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

VenueStudies in Graduate and Postdoctoral Education · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsThematic analysisOriginalityContext (archaeology)AttritionPsychologyScale (ratio)SupervisorSituatedValue (mathematics)Qualitative researchMedical educationPedagogySociologyMedicineSocial scienceManagementComputer science

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to better understand the declines in doctoral students’ mental and physical health while pursuing their doctoral degrees, by revealing the major themes of students’ voluntary comments following a survey that primed students to reflect on these topics. Design/methodology/approach The present study used qualitative thematic analysis to uncover themes in doctoral students’ voluntary comments on a large-scale, web-based survey of graduate students’ motivation and well-being. Findings A thematic analysis revealed six major emerging themes: timing in the degree process, work-life balance, health/well-being changes, impostor syndrome, the supervisor and hopelessness. Research limitations/implications The themes uncovered in the present study contribute to the literature by highlighting important underexplored topics (e.g. timing in the degree process, hopelessness) in doctoral education research and they are discussed and situated in the context of existing literature. Practical implications Implications for doctoral supervisors and departments are discussed. Social implications The present study highlights some pressing concerns among doctoral students, as articulated by the students themselves and can contribute to the betterment of doctoral education, thereby reducing attrition, improving the experiences of doctoral students and possibly affording more candidates to achieve a doctoral degree. Originality/value The present study makes the above-mentioned contributions by taking a novel approach and analyzing doctoral students’ voluntary comments ( n = 607) on a large-scale, web-based survey. Thus, while some of the themes were primed by the survey itself, the data represent issues/concerns that students perceived as important enough to comment about after already having completed a lengthy questionnaire.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.683
GPT teacher head0.640
Teacher spread0.044 · 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