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Record W7161815988 · doi:10.82308/22362

An Investigation into Attrition and Retention of Rehabilitation Professionals

2024· dissertation· en· W7161815988 on OpenAlexaboutno aff
Susanne Mak

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsAttritionStakeholderOccupational therapyRehabilitationWork (physics)Focus groupQualitative researchHealth care

Abstract

fetched live from OpenAlex

Introduction: Health human resources are scarce worldwide. In occupational therapy (OT), physical therapy (PT), and speech-language pathology (S-LP), attrition and retention issues amplify this situation and contribute to the precarity of health systems. The overarching objective of this doctoral research was to investigate why OTs, PTs and S-LPs stayed in, or left their profession. Specific aims were to: 1) understand how educational and health care environments influence professionals’ decisions to stay in, or leave their profession; 2) investigate reasons for attrition across the three professions; 3) explore stakeholder perspectives on attrition and retention; and 4) explore stakeholder-informed retention strategies for OTs, PTs and S-LPs in Quebec (Canada). Methods: The research included three phases. Phase 1 was a scoping review to map the literature on attrition and retention in OT, PT and S-LP. Guided by cultural-historical activity theory (CHAT) as a theoretical scaffolding, phases 2 and 3 used interpretive description (ID) methodology including inductive and deductive analytical approaches and constant comparative techniques. Phase 2 involved interviews with 51 Quebec OTs, PTs and S-LPs. Phase 3 consisted of focus groups with 16 participants from 4 stakeholder groups: employers, professional educational programs, associations, and regulatory bodies. Results: Fifty-nine papers were included in the scoping review. Main findings highlighted push, pull, and stay factors that shaped professionals’ decisions to leave their profession. Based on the interviews, six themes related to professionals’ perceived factors contributing to attrition and retention, were developed: 1) characteristics of work that make it meaningful; 2) aspects of work that practitioners appreciate; 3) factors of daily work that weigh on a practitioner; 4) factors that contribute to managing work; 5) relationships with different stakeholders that shape daily work; and 6) perceptions of the profession. Through a combined analysis of phase 2 and 3 data, five sets of retention strategies were generated: 1) offering informal and formal benefits; 2) ensuring that work aligns with values; 3) improving alignment of work parameters with professionals’ needs and interests; 4) modifying physical, social, cultural, and structural aspects of a workplace; and 5) addressing how the profession is governed. Discussion: Push, pull and stay factors shape professionals’ decisions in terms of leaving their profession. Push factors (e.g., unsupportive environments) drive professionals out of their profession. Pull factors (e.g., career change) draw professionals away from their profession. In contrast, stay factors (e.g., positive impact on clients) support professionals to remain in their chosen profession. System-level factors (e.g., regulatory bodies’ expectations) influenced participants’ decision to stay in or leave their profession. Regarding retention strategies, professionals focused on improving public awareness of their profession while managers targeted individual-level retention strategies (e.g., providing support). Broader solutions were identified by professional educational programs (e.g., mentorship), associations and regulatory bodies (e.g., scope of practice). The data demonstrate that stakeholders need to adopt an intersectoral approach to designing multi-system retention strategies. Conclusion: This doctoral research makes an original and important contribution to knowledge around attrition and retention in OT, PT and S-LP. Using CHAT and ID, the research enriches concepts of attrition and retention, highlights the multi-level, contributing factors to attrition and retention and provides the first set of stakeholder-informed retention strategies. Designing multi-level strategies will be especially important to ensure the availability of professionals for present and future rehabilitation 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)0.036
metaresearch head score (Gemma)0.091
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 score0.372
Threshold uncertainty score0.739

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.091
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.011
Science and technology studies0.0080.004
Scholarly communication0.0060.003
Open science0.0030.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.540
Teacher spread0.440 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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