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Record W2337776651 · doi:10.28945/2327

Come Hell or High Water: Doctoral Students’ Perceptions on Support Services and Persistence

2015· article· en· W2337776651 on OpenAlexaffabout
Melanie Greene

Bibliographic record

VenueInternational journal of doctoral studies · 2015
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPersistence (discontinuity)PerceptionPsychologyGraduate studentsQualitative researchPoint (geometry)Public relationsPedagogyMedical educationPolitical scienceSociologySocial scienceMedicineEngineering

Abstract

fetched live from OpenAlex

While a lack of support has been identified as a contributing factor to non-persistence in graduate studies, there is an absence of literature that matches the provision of specific types of support services with outcomes at the doctoral level. The following questions were addressed in this study: (1) What is the role of institutional support in the persistence and success of graduate students? (2) What do students feel are some of the biggest barriers to graduate student persistence? (3) What do students feel are some of the factors that have a positive influence on persistence? Qualitative methods were employed; eleven interviews were conducted with current and former students who were currently or had previously been enrolled in a doctoral degree program in the social sciences and humanities disciplines. The study was undertaken at a large comprehensive university in Atlantic Canada. Overall findings point to the need to make transparent to doctoral students the role of institutional units and the support services they provide and the need to promote and raise awareness of these services. Five key themes emerged from this study with regards to doctoral student persistence and the role of support services: (1) the unclear role of institutional support; (2) financial considerations; (3) the culture and structure of academia; (4) individual characteristics; (5) support of others. Recommendations for policy, practice, and further research are presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.441
GPT teacher head0.564
Teacher spread0.123 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations30
Published2015
Admission routes2
Has abstractyes

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Same venueInternational journal of doctoral studiesSame topicDoctoral Education Challenges and SolutionsFrench-language works237,207