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Record W7162039535 · doi:10.82308/18752

Exploring the long-term sustainability of a nursing best practice guidelines program

2015· dissertation· en· W7162039535 on OpenAlexaboutno aff
Andrea Ruth Fleiszer

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityBest practiceHealth careNursing practiceNursing careQualitative researchOrganizational cultureNursing research

Abstract

fetched live from OpenAlex

BACKGROUNDDespite advances in knowledge about the implementation of healthcare innovations, little attention has been focused on what happens following the early stages of change. Yet many innovations are not sustained, wasting valuable initial investments and gains. Knowledge gaps related to the sustainability of healthcare innovations are pronounced in nursing, where there is a need to determine how to heighten the "staying power" of practice improvement initiatives.PURPOSEThe purpose of this study was to understand how a nursing best practice guidelines (BPG) program was sustained over a long-term period in an acute healthcare centre.METHODOLOGYI began by conducting a concept analysis of healthcare innovation sustainability. This concept analysis provided a framework to guide a qualitative descriptive case study of an organization-wide nursing BPG program eight years following initial implementation. The case study setting was a tertiary / quaternary urban healthcare centre in Canada. The BPG program was established to improve nursing care practices related to the patient safety challenges of falls, pressure ulcers, and pain. I investigated program sustainability at the organization (nursing department) level, and then across two pairs of embedded, contrasting subcases on inpatient units in the organization. Data sources included 39 key informant interviews (14 organizational, 25 subcase), site visits, and program-related documents. FINDINGSOrganization-level and unit-level findings supported the proposed framework by providing evidence for three characteristics of sustainability (benefits, routinization / institutionalization, and development) and four categories of influencing factors (innovation, context, leadership, and process). At both levels, the combination of the three characteristics was essential; and development of the program and / or its context was increasingly important for program survival over time. Key factors influencing sustainability at the organization level were: commitment of several nursing leaders, complementarity of leadership actions, and leaders’ use of a reflection-and-course-correction strategy. Key influencing factors at the unit level were: perceptions of advantages of BPGs, collaboration, accountability, stability of staffing, linked levels of leadership, attributes of unit leadership teams, and leaders’ strategic use of activities. At both levels, the relationships between characteristics and factors accounted for how the program was sustained.CONCLUSIONSThe persistent, responsive, and complementary efforts of formal leaders, from executive to frontline roles, seem necessary for program longevity. These include strategically-aligned leadership actions focused on the dynamics between teamwork, evaluation, and learning. Such sustainability work appears to be most successful if undertaken as part of managing overall performance. Leaders should consider a broad conceptualization of sustainability that extends beyond program-related benefits and routinization / institutionalization, because development could ensure endurance. Building on the initial framework, theoretical representations of key relationships between sustainability characteristics and factors are provided. These representations can serve to guide research about and to plan for the longer-term sustainability of practice improvement initiatives.

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)0.033
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.086
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.004
Scholarly communication0.0050.003
Open science0.0020.006
Research integrity0.0010.002
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 head0.853
GPT teacher head0.770
Teacher spread0.083 · 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.

Study designQualitative
DomainMethods
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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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