*IMPLEMENTATION SCIENCE > *MASTER 101 > Flashcards

*MASTER 101 Flashcards

(101 cards)

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1
Q

Implementation is system design, not behaviour change

A

Implementation succeeds when systems make the desired behaviour easy, safe, and default. Behaviour change framing over-attributes failure to people instead of constraints, design, and trade-offs.

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2
Q

Behaviour is locally rational

A

People act reasonably given their goals, information, incentives, and constraints. What looks like resistance usually reflects system pressure, not attitude.

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3
Q

Work-as-imagined vs work-as-done

A

Work-as-imagined reflects plans and policies; work-as-done reflects real conditions like interruptions, shortcuts, and trade-offs. Implementation fails when design targets only imagined work.

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4
Q

Burden predicts abandonment

A

Cognitive, temporal, and emotional burden strongly predict whether an intervention is used or quietly dropped. Value cannot compensate for excessive burden.

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5
Q

Champions are fragile solutions

A

Champions compensate for poor design, but their effort is not scalable or durable. Champion dependence predicts collapse at scale or after turnover.

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6
Q

Training is a weak fix for design problems

A

Training adds work and decays quickly. If correct use depends on memory or vigilance, the system is fragile by design.

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7
Q

Complexity scales poorly

A

Every added step, decision, or dependency multiplies failure points at scale. What works locally may collapse when variability increases.

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8
Q

Adaptation is not failure

A

Intentional adaptation can improve fit and equity. The risk is uncontrolled drift that erodes core mechanisms.

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9
Q

Fidelity without fit collapses

A

High fidelity to a poorly fitting design produces workarounds or abandonment. Fit must be addressed before enforcing fidelity.

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10
Q

Sustainment is designed from day one

A

Durability depends on early decisions about ownership, burden, and routinisation. Late sustainment fixes rarely work.

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11
Q

Equity failures show up early

A

Unequal reach, adoption, or burden appear early in rollout. Early inequities usually widen over time if unaddressed.

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12
Q

Silence does not mean success

A

Lack of complaints often reflects overload, disengagement, or learned helplessness rather than smooth implementation.

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13
Q

Pressure hides problems; redesign reveals them

A

Escalation, reminders, and enforcement suppress signals. Redesign surfaces constraints and enables learning.

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14
Q

Defaults beat instructions

A

People reliably follow defaults under pressure. Strong defaults outperform education, reminders, and policies.

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15
Q

Boring is success

A

Stable, unremarkable use without heroics or escalation indicates a well-designed, routinised intervention.

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16
Q

What is an implementation failure mode?

A

A predictable pattern that causes implementation to stall, degrade, or collapse. Failure modes repeat across projects and are preventable.

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17
Q

Training-as-solution failure mode

A

Defaulting to education when the real constraint is workflow, capacity, or design. Repeated training often signals misdiagnosis.

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18
Q

Pilot illusion

A

Pilot success occurs under protected conditions with extra support. Pilots often hide fragility that appears at scale.

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19
Q

Early adopter trap

A

Designing around enthusiastic users instead of average or constrained users. Early adopters mask real usability and burden issues.

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20
Q

Invisible workload failure mode

A

New work is added without removing old work. Hidden effort accumulates until goodwill collapses.

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21
Q

Compliance framing failure mode

A

Interpreting non-use as defiance rather than misfit. Compliance language shuts down learning and increases resistance.

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22
Q

Design-by-policy failure mode

A

Using rules or mandates to compensate for poor design. This creates workarounds and shadow systems.

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23
Q

No-owner failure mode

A

Responsibility is diffused across teams or committees. Without a named owner, sustainment decays.

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24
Q

Handover cliff

A

Implementation collapses when project teams withdraw and support disappears. Handover is a high-risk moment.

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25
One-size-fits-all failure mode
Ignoring meaningful local differences. Uniform demands applied to unequal systems create resistance and inequity.
26
Over-customisation failure mode
Allowing unlimited local variation erodes the core intervention. Too much flexibility destroys coherence.
27
Metrics obsession
Optimising numbers instead of practice. Measurement pressure produces gaming and superficial compliance.
28
Outcome fixation
Focusing on distal outcomes while ignoring implementation quality. Poor outcomes may reflect delivery, not value.
29
Premature scale
Expanding before the intervention is stable. Scaling multiplies defects faster than learning.
30
Context blindness
Assuming settings are similar when they are not. Context explains variation more than motivation.
31
Blame shift
Attributing failure to individuals under pressure. Blame suppresses reporting and learning.
32
Success story bias
Highlighting wins while hiding problems. Polished stories reduce trust and delay correction.
33
Implementation by enthusiasm
Relying on goodwill instead of systems. Enthusiasm fades; systems persist.
34
Strategy inertia
Repeating the same strategies despite poor results. Persistence without learning is stagnation.
35
Ownership drift
Responsibility slowly migrates away from line ownership back to project teams. Drift predicts decay.
36
No exit plan
Launching without planning how support will end. If support cannot end, the design is wrong.
37
What does it mean for implementation to be stuck?
Progress stalls despite effort, pressure, or attention. More activity produces less movement.
38
Low buy-in is a weak diagnosis
It explains nothing about mechanisms. Translate attitudes into constraints to make problems actionable.
39
High adoption but poor fidelity
The intervention is attractive but hard to do correctly. Redesign before retraining.
40
Early enthusiasm followed by drop-off
Novelty without routinisation. Sustainment was never engineered.
41
Rising workarounds are diagnostic
Workarounds show where formal processes do not fit real work. They are data, not deviance.
42
Silence from the frontline
Usually reflects overload or disengagement, not success. Silence is a red flag.
43
Repeated retraining signals misdiagnosis
Knowledge was not the limiting factor. Conditions were.
44
It works in one site fallacy
Success reflects unusually supportive conditions. Single-site success is not proof.
45
We don’t have time
Indicates real trade-offs under pressure. Time complaints are system signals.
46
Heavy enforcement indicates misfit
Policing replaces design when trust and learning are lost.
47
Good reports but poor reality
Metric gaming or fear-driven compliance. Numbers can hide broken systems.
48
We just need more time
Time does not fix misfit, unclear ownership, or excessive burden.
49
Unclear ownership pattern
Tasks fall between roles or teams. Ambiguity predicts decay.
50
Simplification restores progress
When progress follows simplification, burden was the true constraint.
51
What unsticks implementations
Accurate diagnosis of constraints followed by targeted redesign.
52
Sustainment is not a phase
Durability depends on early design choices, not end-stage fixes.
53
Routinisation
A practice becomes the default way work is done without reminders or escalation.
54
Ownership determines sustainment
Someone must absorb cost, maintain standards, and respond to drift.
55
Champion dependence predicts decay
When champions leave, fragile systems fail.
56
Funding props up illusionary success
Artificial support masks true feasibility. When funding ends, use drops.
57
Sustainment drift
Gradual erosion of use or quality over time, often unnoticed.
58
Scale increases variability
As scale grows, context diversity increases and control decreases.
59
Design for scale
Interventions must work without heroics or intensive support.
60
Replication fallacy
Assuming an intervention can be copied unchanged across settings.
61
Protect the why, flex the how
Core mechanisms must remain stable while form adapts.
62
Phased scale beats big-bang
Learning must precede expansion. Big-bang rollouts magnify defects.
63
Fidelity drift at scale
Oversight weakens and adaptation increases. Core must be defined clearly.
64
Mandates create surface compliance
Mandates hide misfit and drive workarounds.
65
Scale readiness
Stable, boring use without intensive support.
66
Presence is not performance
Installed or adopted does not mean used or sustained.
67
Implementation redistributes risk
Every change shifts risk across roles, time, and tasks.
68
Implementation-induced risk
Risk created by how an intervention is introduced or used.
69
Risk increases early
Early rollout is unstable and requires heightened vigilance.
70
Near misses are early signals
They reveal weak points before harm occurs.
71
Risk drift
Gradual movement toward unsafe conditions over time.
72
Escalation must be safe
If escalation feels risky, people will not use it.
73
Soft stop vs hard stop
Soft stops prompt reflection; hard stops prevent continuation. Both must be designed carefully.
74
Blame suppresses escalation
Fear silences reporting and increases risk.
75
Leadership defines safety culture
Leader responses determine whether people speak up.
76
No incidents reported
May reflect fear or normalisation, not safety.
77
Design for safe failure
Assume errors will occur and design containment and recovery.
78
Informal safety practices matter
Peer checking and quiet coordination signal system strain.
79
Scale dulls risk sensing
Distance from frontline slows feedback.
80
Heroics signal system failure
Personal risk-taking compensates for poor design.
81
Reliability is ongoing
High reliability requires continuous sensing and adjustment.
82
HF reframes non-compliance
Deviation reflects rational adaptation, not misconduct.
83
Cognitive load degrades reliability
High load increases error and shortcutting.
84
Workarounds are data
They reveal how systems really function.
85
Functional resonance
Small variations can combine to produce large effects.
86
Standardisation without HF is risky
Rigid standards eliminate necessary flexibility.
87
Fatigue as a determinant
Fatigued systems degrade silently.
88
Culture emerges from systems
Design choices shape norms more than slogans.
89
Observation beats self-report
Real work differs from reported work.
90
Symbolic HF is insufficient
Insight without design change has no impact.
91
What made failure likely?
Shifts focus from blame to system redesign.
92
Leadership shapes conditions
Leaders design the environment, not just the message.
93
Priority means something stops
True priority requires explicit trade-offs.
94
Barrier removal beats speeches
Leaders enable adoption by removing constraints.
95
Pressure without enablement
Breeds workarounds and moral distress.
96
Protect learning early
Premature judgement kills adaptation.
97
Burnout is a leadership outcome
Layering work without removal creates exhaustion.
98
Make trade-offs explicit
Implicit trade-offs fall to frontline staff.
99
Constraint reveals weak design
Under pressure, misfit becomes visible.
100
Fair is not same
Unequal systems need unequal support.
101
Design that fits capacity survives
Interventions that require spare capacity will fail.
*IMPLEMENTATION SCIENCE
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