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Record W2971554504 · doi:10.28945/4135

Examining the Basic Psychological Needs of Library and Information Science Doctoral Students

2018· article· en· W2971554504 on OpenAlexaboutno aff
Africa S. Hands

Bibliographic record

VenueInternational journal of doctoral studies · 2018
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)AutonomySelf-determination theoryPsychologyGraduate studentsMedical educationPedagogySocial psychology

Abstract

fetched live from OpenAlex

Aim/Purpose: The purpose of this study was to examine how the basic psychological needs of self-determination theory are reflected in doctoral students’ motivation to earn the PhD. Background: As isolating as the doctoral experience seems, it is one that occurs in a social-cultural environment that can either support or hinder the student. This research highlights the motivational influences of library and information science doctoral students regarding experiences of autonomy, competence, and relatedness. Methodology: Qualitative data were collected from seven (7) enrolled doctoral students at library and information science programs in the United States and Canada. Transcripts from semi-structured interviews and students’ personal admission statements were subjected to deductive content analysis for emphasis on three basic psychological needs: autonomy, competence, and relatedness. Contribution: Findings illustrate the role faculty play in student motivation and satisfaction with the doctoral experience. There are implications for faculty, mentors, and advisors working with current and former graduate students who may be considering a PhD. The findings have implications for doctoral recruitment, advising, and student services of interest to faculty and administrators across disciplines. It also shows the applicability of self-determination theory in the examination of the doctoral student experience and overall motivation. Findings: Deductive analysis based on self-determination theory (SDT) demonstrates factors related to self-determination theory’s basic psychological needs – autonomy, competence, and relatedness – as relevant to participants’ motivation to pursue a doctoral degree and to the examination of doctoral student initial motivation. Doctoral students are motivated by multiple factors including their interactions with and encouragement received from current and former faculty. Students report experiences related to autonomy, competence, and relatedness that energized them to pursue a doctoral degree and that have positively influenced their doctoral experience thus far. Recommendations for Practitioners: Faculty and program administrators may use this data to inform their understanding of the expectations of today’s doctoral students and motivational drivers of prospective students and to tailor support services accordingly. Recommendation for Researchers: This is a preliminary investigation of doctoral student motivation in relation to the basic psychological needs. More research is needed on a larger sample of students to more fully understand the influence of autonomy, competence, and relatedness on doctoral student initial and ongoing motivation. Impact on Society: This research is an important step in bridging faculty and student perceptions of what is important to their initial and ongoing enrollment in a doctoral program. By improving students’ experiences of autonomy, competence, and relatedness, it may be possible to improve the overall doctoral experience leading to completion of the PhD. Future Research: Future research will expand to include doctoral students farther along in their doctoral programs, the administration of the Basic Psychological Needs Scale, and may examine faculty perceptions of the three basic psychological needs.

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)0.009
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.404
Teacher spread0.305 · 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 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

Citations9
Published2018
Admission routes1
Has abstractyes

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